Personalised medicine – treatments which focus on the individual patient or sub-population, not simply prescribing a generic treatment to a large swathe of patients – has been a key target in healthcare for a long time now. New technologies, including machine learning (ML), have the potential to greatly improve how patients are treated individually by better analysing their data and reducing industry costs.

What is personalised medicine?

Personalised or precision medicine is an approach which classifies patients based on a number of individual patient characteristics, such as biomarkers, from their disease subtype, to risk, to response to treatment. 

The importance of personalised and precision medicine lies in the fact that expenditure in healthcare can be greatly reduced by implementing it, alongside huge benefits to the patients who have their disease treated as it specifically relates to them and their population sub-type; they do not have the faults inherent in a drug or product that caters for a broad, ultimately highly variable population. 

While personalised medicine is currently largely evaluated using the genetic data and biological markers of the patient, as well as some work around personal preference and needs, more must be done to predict individual outcomes of certain treatments and resulting adverse effects they might have. This can be done with predictive modelling. 

Predictive modelling takes historical data and uses AI/ML and statistics to determine possible outcomes from it. This can mean analysing an individual’s health conditions, age and ancestral medical history to determine their risk of developing certain diseases. Naturally, this is an extremely complex process, with a number of variables to be quantified between data and outcome. Humans alone could not create these models. With increasing sophistication and speed, however, algorithms are able to do so.

Healthcare data can come from a number of sources. It can be personal patient data, health records such as hospital electronic health records (EHRs), or imaging data that can be scanned by software to determine best course of action for the patient. 

From these models, true personalised medicine can be achieved. With a better understanding of how likely it is for patients to develop a diagnosis or recover from a disease, pharma and healthcare companies can determine the best possible treatment to suit their condition and fit their needs. 

Benefits of predictive models in personalised medicine

The University of Pennsylvania was one of the first research organisations to develop a predictive ML model for healthcare in 2017, using EHR data to identify those patients most likely to contract septic shock 12 hours in advance of their doing so. Other such ML models have followed, such as the FDA’s tool using genetic information and past studies to predict patient response to therapies. 

Training ML algorithms on various healthcare data sources can ensure an incredibly fast, largely accurate prediction of how a patient will respond to treatment given their past history, similar population reactions, and other biological and environmental factors. This in turn can greatly reduce risk to patients, who otherwise might end up taking drugs that would cause major adverse effects or not work at all – or perhaps even the wrong drugs, which could make their conditions worse.

Across the pharma and healthcare industries, these models have the potential to vastly enhance patient care while reducing the cost of failed or inefficient treatments. According to one study, 60% of healthcare executives believed predictive analytics would save their businesses 15% or more over five years; in another, 92% of payers and providers believed predictive analytics would be ‘essential’ to their businesses’ futures. 

There is also the potential for gamification of healthcare, an area that has shown great promise in other industries. With knowledge of potential illnesses and susceptibilities, patients can in theory use this information to guide their lifestyle to avoid the greatest threats, thus ultimately staying more healthy and causing less expense to the healthcare sector. 

Challenges to building predictive models

The first and most evident challenge when attempting to build a predictive model in the pharma space is access to data. This can be extremely difficult depending on the source, as many companies’ data is heavily siloed with different departments or simply hidden from public view. Naturally, many organisations also restrict data access due to concerns such as patient privacy or restrictive regulations. 

One solution to this problem of siloing is federated learning. This form of ML training is wholly decentralised, meaning that rather than data being brought to the central ML server and fed in, the ML algorithm has local copies installed on every device or server hosting the data it needs, whether patient wearables or EHRs. Then, rather than the data travelling to the central ML server to be analysed, the local copies of the algorithm train themselves on the data and transfer back training results to the central ML algorithm – meaning that no data is jeopardised or risked. 

Another inherent challenge when working with AI and machine learning is the need to train algorithms correctly to interpret the data they are given. Naturally, this is a complicated process that varies per algorithm, but according to the Center for Applied AI four steps can be taken to ensure success: 

• Understanding the goal of the algorithm being built and focusing on it

•Being specific in the target you are aiming at

• Preparing to either update or terminate the algorithm if it fails to live up to the goals set for it

• Conduct regular audits on the algorithm to determine value

A final challenge involving the creation of predictive models regards inclusivity and representation. As so much of pharmaceutical data is skewed in favour of some patient populations and races at the expense of others, it can be hard to gather enough broad data to ensure there is no bias when it is fed into the algorithm. 

It is certainly true that predictive models are not yet at the stage where they can be employed on a large scale, and not to a reliable enough extent to warrant their cost. To truly see this technology develop, greater computational efficiency and better access to large-scale datasets will be needed – not to mention a reduction in business data silos, and greater collaboration within the pharma and healthcare industries. 

But it is clear that predictive models have the potential to enormously benefit the sector. With the ability to predict how patients’ diseases will develop or even prevent them indefinitely, healthcare cannot afford to let this technology pass by.

Joshua Neil, Editor
PharmaFeatures

The post How Predictive Modelling Can Change Personalised Medicine appeared first on Proventa International.

Mental health conditions like addiction and diseases like Alzheimer’s represent a great unmet clinical need. Psychedelic research at preclinical stages have shown potential for positive neurological changes in the brain for models of depression, addiction and Alzheimer’s. In a recent interview with Proventa International, Assistant Professor Albert Garcia-Romeu, from the Johns Hopkins Center for Psychedelic and Consciousness Research, discussed the benefits and challenges of psychedelic research.

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Proventa: Would you mind introducing your background in the field and your personal targets for research?

Albert Garcia-Romeu: I am an assistant professor at John Hopkins School of Medicine where we have established a Center for Psychedelic and Consciousness Research. A lot of my research experience here has focused on studying the effects of psilocybin in various populations. We are particularly interested in how psilocybin can be potentially used in a therapeutic fashion – for instance in treating conditions such as tobacco addiction. Another area I’ve been working on more recently has been examining psilocybin in patients with early-stage Alzheimer’s disease. 

These are the two areas I’ve mostly focused on for the last nine years. 

How translatable do you believe the preclinical models of psychedelic drugs are to the human brain?

AGR: For some things [animal models] are very good and useful. But when we’re talking about people and their experiences, I don’t think it translates as well – you often have to coax animals to become addicted to drugs over time in a small environment with little stimuli. People, on the other hand, have to deal with other stimuli like social interactions and personal issues that don’t necessarily translate to animal models. 

For example, with rodent models of depression, how do we know whether rodent behaviour is translatable to how a human feels when they are depressed? Or when a person has lost their job or a loved one?

[Animal models] are very useful for looking at molecular changes, e.g. in parts of the brain or circuits or synapses. It can provide a window into some of these higher level changes – though I think some of the higher level functions like sense of self may not be fully translatable. If you look at medication development, everyone has heard a number of times about drugs which might restore memory in mouse models of Alzheimer’s, but don’t work at all in the transition to human trials. We are a lot more complicated than animals – although animals are still pretty complicated. 

They [animal models] definitely give us great insights that we wouldn’t necessarily be able to achieve from human work, however.

Could you describe the mechanism as to how psychedelic compounds like psilocybin could help patients with MDD for example, or an addiction?

AGR: We don’t exactly know the answer to the first part of that question. We know these drugs have been used for thousands of years by people – so what we do know is they are psychoactive and they create these very powerful mood-altering effects, altering our sensory perception, sense of self, and perception of time. These features are consistent with use in ceremonial or spiritual practices among indigenous cultures. 

More recently, psychedelics were used within the counterculture in the 1960s. At the time, people called these drug experiences “mind-expanding”. So psychologically, the experiences people undergo on these drugs often reflect the desired effects we might seek from conventional therapy like generating insight and getting new perspectives. These experiences can change the way we think about ourselves, our lives. 

At our laboratory, we use primarily high doses of psilocybin to create very powerful experiences that can promote psychological and behavioural changes. Sometimes these experiences can have existential or spiritual overtones and this can help people reflect and look back at recurring patterns in their lives and mental landscapes. This is more on the psychological side, but we have also seen a great uptick in research and science investigating how these drugs are altering brain function when people are on these drugs. 

What’s very odd about the psychedelics however, is that they also have an ongoing persistent signal which can occur a day later, a week later, a month later – showing that the brain is still functioning differently than before the drug was taken. 

And so that seems to be associated with some form of mental reset, the experiences people have are changing the way they process emotions. For instance, the activity of the amygdala has been shown to change after psilocybin, which has also been linked to antidepressant effects and positive emotions. These are some of the neurobiological mechanisms that we are starting to learn more about. In terms of the neuroscience, we are only just starting to scratch the surface of both cellular changes that are happening in the brain, such as increased synapse formation in areas like the prefrontal cortex and the hippocampus, and epigenetic changes that seem to occur after a single dose of LSD or psilocybin. 

Again those are data not necessarily based on human research but looking at animal models, and cellular/molecular systems. However, they’re giving us more information as to what’s happening at the smaller units of the brain which could be fostering these long-lasting changes. 

One of the problems with current treatment for psychological problems like depression is that prescribed drugs are typically a short-term option – is this something you believe psychedelic drugs could overcome, inducing more long-lasting changes in the brain?

AGR: We hope so, but we can’t say that for sure. The weird thing about people is that we’re not this passive structure like a puzzle. We’re constantly re-building our own little puzzles, moving things around – the behaviours we undertake, the way we think about the world, whatever we’re putting into our system is constantly pushing our bodies and brains to re-write themselves. 

So I think that with supportive therapy combined with psilocybin, we have the ability to help a person communicate where they’re at, where they’d like to be going, where the roadblocks are. And the drug experiences themselves can provide a catalyst to make them feel and think differently in the moment, but then to support that in the longer term by actively attempting to make these changes in the way we’re thinking, the way we relate to ourselves and others, the way we are behaving in the world, which can be further encouraged with careful integration after the drug effects wear off.

And so when we do that, we provide a scaffolding, if you will, to allow the brain to become more permanently re-wired. This is a little different to the model treatment-wise from what you would see with standard antidepressants, and certainly the experiences induced by the drugs are very different when comparing psilocybin with other medications. 

What considerations should be taken into account if these drugs reach the market, with regards to the age of those using them (especially so if permanent changes in the brain occur)?

AGR: That’s a good question. I don’t really know exactly where regulators will draw the line there. Drugs like these would initially be confined to adults at the start and then you could possibly move into younger patients like adolescents eventually, though I think this will have to be done very carefully, and I don’t foresee this happening anytime soon. 

I think for now we’re squarely focused on adults – 18 or 21 years old are the usual age cutoffs in research. One of the areas that needs to be addressed if these drugs reach the market, is how to approach studies to ensure people have these experiences safely and ensure there is appropriate training of people monitoring these experiences and managing them therapeutically. 

How significantly will regulatory restrictions impact research and production of psychedelic drugs?

AGR: I mean it’s made this a very slow process. The lab I’m working at has been doing this for two decades and people weren’t really doing this in the US before that very much because of all these restrictions. Looking 30, 40 years ago, there was almost no research in this area because of regulatory restrictions.

It’s been a hurdle that’s been very difficult to overcome, but I think the regulatory bodies in the US at least are trying to encourage research that has potential for therapeutic development. And again it becomes complicated when companies try to patent what is a natural substance like psilocybin, so then people have to figure out how to create this in a marketable way to get some sort of ROI and that becomes a whole other problem. 

In your opinion, why do you think psychedelic research is receiving so much attention and investment? Is it the potential to address some of the challenges with current treatment?

AGR: You know, I think science is very much fad-driven. One area will be fashionable while another is not. There’s been huge interest, and obviously a lot of people want to try to catch that gravy train and make money. 

Part of the problem is our culture and society have developed in ways that can be unhealthy for us mentally, and in many ways, what we’ve been using have been more stop-gap treatments. The types of medication that we have don’t work for everyone and have side effects. Now we’re seeing people that want to turn psychedelic medicines into a profitable venture. We’ve seen this with cannabis – as soon as legalistion occurred, you had people turning up saying, “well this is legal now so we’re going to grow it and sell it to you for a profit” and so it’s a weird kind of dynamic to see that happening at a very rapid rate with psychedelics. 

There may be a time when we can source psilocybin in the field, people could grow it in their gardens at home so we don’t necessarily need some manufacturer selling us the pills. But the next sort of arms race will be people using machine learning algorithms and AI to try to build molecules that they say “this is going to work better than psilocybin”, or “this acts faster, so you don’t have to sit there for six to eight hours, you can get the same benefit in 15 minutes”, or “my formulation is so good because it still produces what we see with psilocybin i.e. healthy synaptic changes but it doesn’t have any psychedelic action, so you can have it while sitting at your desk at work”. It’s all a way to better market it, but we’ll have to see how the actual clinical research turns out in the end.

My take is that psychedelic treatments can really help us fill an important need though, which is why we’re seeing so much interest. I think these substances can help us to spend more time on our own internal experiences, processing difficult emotions like grief or trauma that we’ve swept under the carpet, and working with a therapist to figure out how that plays into our larger life. And this stands in contrast to more standard available medications, where the drugs can help treat the symptoms, but not always the underlying cause. So, I hope this can lead to a larger shift in our mental healthcare systems that lead to better care overall.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post The Potential of Therapeutic Psychedelics Drugs: An Interview with Professor Albert Garcia-Romeu appeared first on Proventa International.

While significant progression has been made in cancer therapeutics, the disease remains a serious health risk. The lack of commercially available diagnostic biomarkers hinders accurate early cancer diagnosis which could otherwise save many lives. Protein-based biomarkers have demonstrated the potential to introduce non-invasive diagnostics for cancer patients. 

For daily articles on the latest pharma trends and innovations, as well as interviews with leading experts and in-depth industry White Papers, subscribe to PharmaFeatures.com.

Hear from some of the industry leaders including Aleksandra Filipovic who will be providing her expertise in leading a discussion on assessing the industry shift towards protein-based diagnostic biomarkers. To discuss these innovations and more with other leading experts in an informal setting, sign up to Proventa’s Oncology Strategy Meeting, held online on 17 June 2021. 

Biomarkers are an important tool in precision medicine across a number of therapeutic areas. In oncology, genetic profiling technology and targeted cell therapies have seen biomarkers play an important role in disease management for cancer patients. 

A cancer biomarker refers to a “tumor characteristic or a response of the body in the presence of cancer that can be objectively measured and evaluated”. Circulating tumour markers found in bodily fluids like blood and urine are currently implemented in clinical investigations. These biomarkers are often used to measure the risk of developing the disease, response to therapy, and estimate prognosis. 

The prominent issue however is that “although an elevated level of a circulating tumor marker may suggest the presence of cancer… this alone is not enough to diagnose cancer”. This represents an unmet clinical need to identify and validate a diagnostic biomarker that can be used alone (i.e. not in conjunction with other methods) to diagnose cancer types.

Cancer diagnostics based on protein biomarker detection have shown obvious potential for a number of years. For example, serum protein expression is often significantly raised in patients’ samples which are affected by colon and other cancers. 

In the last decade, protein-based diagnostic biomarkers are receiving increasing clinical attention due to their implication in cancer pathology and potential for non-invasive detection. 

Protein-based diagnostic biomarkers

In order to utilise protein-based biomarkers, there must be significant proteomic changes that occur specific to cancer. Mutations in cancer-associated genes, for example, can be manifested in defective protein structure. These defects can impact protein stability and cause the protein to become more susceptible for degradation. 

Proteomic studies into colon cancer have identified a number of protein biomarkers for potential diagnostic and therapeutic applications. “The over-expressed glycoprotein is responsible for tumor growth and spread into distinct parts.” The secreted protein has also been associated with a number of malignancies including the stomach, prostate and liver. 

As of today, there are no protein-based biomarkers that have surpassed clinical studies for application in clinical practice. However, in the last decade, researchers have been focused on continuing to develop assays to more accurately detect potential biomarker candidates in comparison to traditional techniques. 

One example is the proximity extension assay (PEA) which was used in a 2019 study identifying plasma protein biomarker signatures for ovarian cancer. 

PEA technology is high-throughput fluid protein biomarker detection immunoassay. The system is based upon a “unique antibody-oligonucleotide protein binding for quantitative real-time polymerase chain reaction (PCR)-based measurement”. The amplification by PCR and hybridisation of a labelled probe to its DNA target generates a highly sensitive detection of proteins, protein modifications or protein-protein interactions.

Hybridisation, in the context of assays, involves the labelling of nucleic acid which aims to identify related DNA/RNA within a complex mixture of unlabelled nucleic acids.

In this aforementioned 2019 study, it was emphasised that a non-invasive diagnostic test with higher sensitivity and greater specificity could be used to distinguish between women with malignant and benign masses. The benefit of this could prevent over-diagnostic surgery which can be unnecessary and introduce health complications. 

A 2020 study chose to use mass spectrometry-based high throughput screening in order to identify protein biomarker candidates detected in the human blood. The aim of the study was to develop a panel of proteins which can distinguish patients with lung cancer from health donors via plasma samples. 

The results from the assay identified six blood-based proteins which could be used as a potential diagnostic tool in lung cancer. This is an important step forward in identifying biomarker candidates which could be validated and potentially be used for clinical practice. Furthermore, “the 6-protein panel non-invasively detected lung cancer at different stages of the disease (including stage I), suggesting its high potential as a screening tool”.

Challenges

The analytical challenges of screening the vast number of proteins is one of the major stumbling blocks for protein-based biomarker screening. Heterogeneity refers to the level of diversity within individuals. In the context of proteomics, it refers to the magnitude of proteomic variations between normal individuals and different disease states. 

The implication of heterogeneity makes it difficult to standardise protein-based biomarkers for an entire patient population due to proteomic diversity. 

A recent approach in the last few years has been developed to try and circumvent the challenge of tumor heterogeneity. The combination of matrix-assisted laser desorption ionisation with imaging mass spectroscopy allows “proteomics-based studies to provide both patient-specific and cancer-specific information as a means for biomarker discovery and cancer tissue classification. It also provides morphology-based proteomics analysis for cancer tissue”. 

Unfortunately, several technical challenges still exist including low signal-to-noise ratio and low mass accuracy. 

Validation of cancer biomarkers has proven to be a challenge that has hindered progress in reaching the clinic. To meet statistical requirements for a potential protein biomarker, patient samples will be required to expand further accompanied by excellent clinical data. This is not always feasible as patients are not always comfortable with samples being used in clinical studies nor do they appreciate the biopsy procedure. 

One suggested solution to find and validate new biomarkers is to develop biobanks with numerous consecutive samples collected over time from each of large numbers of individuals. A biobank is a “collection of human biological samples and associated information organized in a systematic way for research purposes”. 

In addition to supporting validation, biobanks could also help define the normal protein biomarker concentration ranges. These ranges frequently differ across the population due to genetic and environmental factors, which could provide more information about the level of proteomic diversity with regards to specific cancer types. 

While research needs to overcome the challenges of biomarker validation and heterogeneity, protein-based diagnostic biomarkers show promise. The development of accurate, non-invasive diagnostic tools would allow patients to receive rapid and accurate diagnosis to support clinical decisions for personalised therapies. Furthermore, it could prevent patients receiving unnecessary surgery which could impact their mental and physical health.

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Oncology Strategy Meeting, set for 17 June 2021.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post Protein-Based Diagnostic Biomarkers for Cancer: Where Are We Now? appeared first on Proventa International.

COVID-19 has changed how many sponsors enrol and retain trial participants. From decentralisation, increased use of wearables and changes in population targeting, the pandemic has seen a shift in both priorities and procedures with regard to enrolment. But is this enough to overcome issues of minority underrepresentation and population disenfranchisement that clinical trials struggled with prior to 2020? We spoke with Ash Rishi, CEO of COUCH Health, about the problems of patient enfranchisement and recruitment, and what can be done to overcome them.

For daily articles on the latest pharma trends and innovations, as well as interviews with leading experts and in-depth industry White Papers, subscribe to PharmaFeatures.com.

Tell us about your company’s work, and the causes it supports?

Ash Rishi: I’m the founder of COUCH Health, a patient engagement agency working globally. Over the last few years, one of our core missions has been improving diversity and inclusion within clinical trials.

A lot of our research has centred around determining the best way to do this. Often, this has involved talking to community and spiritual leaders, but I think there’s more we can do. COUCH Health reaches patients through digital marketing, aiming to get them into clinical trials. We’re trying to evolve from a generic advert that’s sent to every population to something more bespoke, e.g. ensuring our language is understandable and relevant to every community. Our challenge is doing that on a global scale. 

To me, this is also a personal challenge. I lost my father in his early fifties to cancer, and I’ve often wondered whether, if he had taken part in a clinical trial, whether my mother and I would have had a few more months with him. Now I run my own company, I hope I can do something to ensure this is a reality for others out there. 

What are the main causes of underrepresentation in clinical trials? To what extent is this a fault of trial sponsors?

AR: It’s a healthcare and health equity issue more than a clinical trial one. You often see what we in the UK call the ‘postcode lottery’. Those in richer boroughs or areas get more innovative treatments, while those of lower socioeconomic standing aren’t getting the treatment they need. 

There’s also the issue that certain communities have a deep mistrust of healthcare, due to political issues such as Brexit or the Windrush scandal in the UK. These have all created an environment of distrust. In the US, there’s the additional issue that healthcare is expensive, and therefore those of lower socioeconomic status have less access. 

Clinical trials have also been designed in a very labour-intensive way. You can’t just have an assessment over the phone; you need to take a day off and go to a site that could be over 100 miles away. If you’re working two jobs, or have to look after several children, this is extremely challenging. Even if you do join a study, it’s doubtful you’ll stay. There absolutely needs to be a revolution in how these trials are run — and hopefully, COVID-19 is bringing about that change.

Is this a case of certain specific therapeutic areas lacking subject diversity, or is it more an overall healthcare / structural problem?

AR: It’s an issue across the board — not any one area in particular. There are some good examples when we talk about female underrepresentation. Cardiovascular disease, which affects men and women equally, has enormous disparity in clinical trials. Even more extreme, Cialis, the ‘female viagra’, would presumably have 100% female trial participants, but 80% of them are men! 

The same can be said for over 65s. Our population is ageing, and this is an important population to target, but they’re not included in trials, either for Alzheimer’s or other conditions. How can we know medications work if we’re not testing them sufficiently? 

Are certain populations more affected by these problems, and to what extent is this reflected in that population’s health? 

AR: The populations most affected differs by country. Obviously, more data has come out of the US than any other country, and so we know that there it’s mainly Black, Hispanic and Native American populations. It’s been reported that Black Americans make up 18% of the US population, and yet represent only 5% of clinical trials. 

We want to reach a place where participants on a specific trial represent the demographic affected by the trial target. Sickle cell disease, for example, affects 60% African Americans, so 60% of the trial demographic should be Black. That’s what we’re trying to encourage sponsors to think about. It’s not just pushing the overall message of recruitment. It’s about engaging specific populations through language and other means. 

What else can organisations do to increase patient-centricity and work with these populations in recruitment?

AR: A few initiatives are working well. We’ve developed cultural safety training. Research sites, which are the element patients interact with most, are trained up on how to be culturally safe, beyond unconscious cultural bias and cultural competency. 

We’ve found that with the reflection exercises on this program, individuals have a lightbulb moment where the get what ‘culture’ means. To put it crudely, many people think white people don’t have a particular culture. But they do, and when people understand their own culture then we can flip it round and apply it to other populations. 

As I mentioned, we’re also doing community outreach, where we have materials reviewed by the population we’re trying to reach. This is an important step, because medics and scientists don’t speak in the language of the population. By making the literature more culturally accessible and making the visuals more diverse and representative, we can appeal to populations in a positive, focused way.

Resource-wise, the FDA has brought out guidance recently. In the UK, we’re trying to encourage regulators to make this policy, though we’re probably lagging a little, honestly. But there really are few resources out there. We’re getting case studies soon, however.

In one of your reports, you mention age as a factor in minority reaction to trials and vaccines. Will younger generations see a natural shift in minority representation in trials?

AR: It’s my hope that new generations will see a shift towards representation. Technically, it should improve. Media representation of trials during COVID has been an important step in this. We’re now seeing a better portrayal of trials. But I’m still seeing adverts that miss the point, that are only creating more mistrust. I’m worried we’re making the same mistakes again. Targeting a minority because they’re a minority creates mistrust, because it’s not well understood that there is an issue of diversity in trials!

COVID-19 has dramatically shifted how organisations recruit patients. Have lessons been learnt in terms of diversity and representation? Have there been any negatives to this?

AR: COVID-19 was absolutely an industry eye-opener. People realised that the virus affected populations differently quite early on. Through the trial process, companies like Moderna ensured representation was diverse. 

There were large numbers of trials working to produce a COVID-19 vaccine, and only a few got their products to market. The common denominator for the successful trials was that they generally had stronger data, and this came from more diverse trial populations. There really weren’t many negatives to this pandemic, from a representation perspective.

This proves they can do it! I think the industry has finally woken up a little bit to innovation, new approaches and models, and we will see a boom in this industry, and in the next 2–3 years we’ll see some real and needed innovation. 

To better raise awareness of the diversity and inclusion issue within clinical Trials, Ash has been running Demand Diversity, which is a campaign for change. A mission to raise awareness. And a rallying cry for the industry to do better.

Guided by insight, research and vital collaborations with patients, diverse groups and others in the industry with the same vision, we’re going to drive action and demand that we all take responsibility to do better. You can get exclusive research here.

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Clinical Operations Strategy Meeting, set for 15 June 2021.

Joshua Neil, Editor
Proventa International

The post Supporting Diversity in Clinical Trials: An Interview with Ash Rishi, COUCH Health appeared first on Proventa International.

The success of decentralised clinical trials during the pandemic was dependent on telemedicine to monitor patient health and collect data. Actigraphy bracelets and video consultations are a few examples of how patients can be monitored remotely for healthcare and data collection for clinical trials. While remote patient monitoring has come a long way in the last few years, there continue to be a number of challenges that need to be addressed. 

Decentralised clinical trials work on the basis that patients are no longer required to visit clinical trial sites. In comparison to traditional clinical trials, patient recruitment and consent were all facilitated electronically. In the case of the COVID-19 pandemic, decentralised trials were essential to continue research. CROs needed to develop methods of monitoring patient health as well as collecting data virtually. Remote patient monitoring is described as “the advances in information technology used to gather patient data outside of traditional healthcare settings”. Telehealth, on the other hand, refers to the actual technology that enables RPM, like a Fitbit watch. 

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Wearable devices 

In order to facilitate remote patient monitoring, a number of wearable devices have been developed to accurately record health data as part of patients’ daily routine. 

Actigraphy bracelets 

Actigraphy devices are a form of telemedicine typically worn around the wrist. While they can be used to measure heart rate during fitness, they are also used to record the sleep cycle via changes in muscle tone.  

In the past, clinical trials investigating sleep disorders or disturbances have relied on polysomnography (PSG), a type of sleep study recorded in a lab. However, in the last few years an increasing number of sleep studies are utilising actigraphy bracelets as an alternative to the traditional sleep lab for remote monitoring. 

A 2020 study assessing the comparability of actigraphy bracelets vs PSG in sleep assessment found that sleep quality metrics of sleep period efficiency and wake-after-sleep-onset computed by ACT-S1 (actigraphy) were not significantly different from PSG-EEG. According to the team, the results provide evidence of “promising performance of a full-automation of the sleep tracking procedure with ACT-S1 on older adults”. 

In comparison to PSG, actigraphy is a less clinical and non-invasive measurement of sleep in a more natural environment over a long period of time. PSG is also a greater financial burden due to the limited period of measurement and the cost of running a sleep unit, specialized technicians and expensive equipment. 

Actigraphy bracelets have proven an important tool for clinical research involving mentally vulnerable individuals. One of the most reported symptoms in veterans with PTSD is a significantly impaired sleep regime, whether it be nightmares or insomnia. Therefore, sleeping in an unknown environment like a sleep lab could trigger anxiety, especially where trauma is associated with the dark. Actigraphy enables the recording of the natural sleep cycle at home, reducing mental distress that could harm the patient and compromise the reliability of the data. 

Telehealth visits

Clinical consultations over video conferencing is another example of telemedicine utilised in clinical research and healthcare settings. Telehealth visits are a valuable tool for two reasons: firstly, it reduces the cost of funding a location for clinics and travel reimbursements. Secondly, it reduces the burden of the patient travelling to sites for multiple visits which can be time-consuming and not always easy if transport is not accessible.  

A 2017 case study supports the value of telehealth. In this case, a clinical trial participant with stage 4 non-small cell lung cancer was able to continue receiving care and monitoring throughout treatment using video consultations. The use of telehealth proved to be useful as the patient was located rurally, so may not have had easy access to the healthcare.  

Challenges

While the benefits of telemedicine are evident in the latest research, there are several challenges that need to be addressed. Remote patient monitoring requires collection of health data virtually using telemedicine devices rather than in-person at the clinic. This presents obvious data security concerns with regards to the risk of cyber attacks due to health data transmitted over wifi connection for example. 

The significant privacy and security risks in telehealth systems have been suggested to adversely affect patients’ and clinicians’ level of trust and willingness to adopt and use the system. The secure storage and processing of sensitive data such as electronic medical records and clinical trial consent is of paramount importance to reinforce trust between clinicians and patients. 

Block chain technology is emerging as a popular choice in clinical research for data security. The basis of blockchain is a large datastore which is incorruptible and traceable, spreading data over large datastores, rather than a single platform. The benefit of this is that the confidential data collected virtually is less vulnerable to hacking or infringement. 

The digital divide

The digital divide is another concern for remote patient monitoring. This specifically refers to the difficulty of certain populations using modern technology or the internet. This is an issue for data collection, as it may not be recording the required information or may be tampered with by accident. In addition, those with limited understanding of modern technology may become frustrated with devices which could lead to drop outs. In terms of video consultations, technical difficulties such as internet problems or poor software knowledge can create additional stress for patients. 

The digital divide continues to present an issue for many groups including the elderly population, patients in rural locations, and poorer individuals with limited access to the internet devices. According to a 2019 study, 21 million people in the US still lack broadband access. This emphasises how specific populations are potentially missing on participation in clinical research using telemedicine, and raises the question as to how remote patient monitoring can be adjusted to address this issue.  

The shift to telemedicine in clinical research has demonstrated the feasibility of health data collection outside of the clinic, reducing a significant amount of time and resources. In addition, the patient-centric approach of remote monitoring enables the recording of real-world data, a valuable part of clinical trial outcomes. Finally, the rapid adaptation to digital platforms has demonstrated how the industry is evolving to “deliver approaches that reduce patient burden, increase patient engagement, and promote trial continuity”.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post The Opportunities and Challenges of Remote Patient Monitoring appeared first on Proventa International.

Precision medicine is a rapidly growing therapeutic area within oncology. Technological innovations have contributed to the refinement of predictive and diagnostic techniques used to tailor therapy for cancer patients. The goal is to not only develop more precise treatments, but to also predict the optimum choice of therapy to maximise the chance of progression free survival.

For daily articles on the latest pharma trends and innovations, as well as interviews with leading experts and in-depth industry White Papers, subscribe to PharmaFeatures.com.

Deep learning for diagnostics 

Deep learning is a specialised area of ML that attempts to model abstraction from large-scale data using multi-layered deep neural networks (DNNs). Abstraction refers to the process of filtering out irrelevant data in order to focus on the desired information. Cancer diagnosis relies upon accurate visual pattern recognition in images and large data sets in order to identify features that cause concern. While oncology physicians are highly specialised and trained in their field, there is only so much data processing that the human brain can handle. Machine learning on the other hand can be programmed to identify abnormalities in vast data sets with greater accuracy land speed than the average human. 

Deep learning has been used for image classification in cancer diagnosis. Such image tasks require a form of deep learning known as a convolutional neural network (CNN). This artificial neural network relies on many layers composed of connected artificial neurons that perform mathematical operations on input data. One of the main reasons why CNN works well for image classification is the ability of the network to mimic the natural visual processing of the human brain “which enables the interpretation of dense information such as the relationship of nearby pixels and objects”. 

During training, labeled image data is inputted into the artificial neural network and undergoes two processes, known as filtering and sub-sampling. These processes enable the network to learn the image features. Labelled image data may comprise images of skin lesions with cancerous features identified. Once a model is trained, it is validated in an independent rest in order to evaluate its final performance.

In 2020, a study published in Nature Research used deep learned tissue “fingerprints” to classify breast cancers. One of the challenges of using deep learning for histopathology was noted in the study as the lack of large, well-annotated data sets required for the algorithms to learn statistical significance. After training the algorithm, they used “the features the network learned, called ‘fingerprints,’ to predict ER, PR, and Her2 status in two datasets”. While the dataset for the study was small, the fact that the fingerprints determined different growth factor status from whole slide images of breast cancer. This supported the potential of using ML in cancer diagnosis. 

Next-generation sequencing 

Next-generation sequencing (NGS) is a DNA-sequencing technique used for high-throughput tumour profiling. With the ability to sequence the entire human genome in a day, NGS offers a significant advantage over traditional genomic sequencing like the Sanger sequencing. According to a recent review, NGS comprises of the following steps:

• Each DNA fragment to be sequenced is bound to a structure called array, followed by the enzyme DNA polymerase which adds labeled nucleotides sequentially. 

• A high-resolution camera captures the signal from each nucleotide as it becomes integrated and notes the spatial coordinates and time. 

• The sequence at each spot can then be inferred by a computer program to generate a contiguous DNA sequence, referred to as a read.

NGS is an important part of genetic sequencing within a diagnostic technique known as liquid biopsy. Liquid biopsy is a non-invasive and real-time monitoring of disease development, which can be applied to all stages within cancer diagnosis and treatment. Liquid biopsy measures the presence of circulating tumour DNA (ctDNA). cTDNA is a type of cancer biomarker used to detect the disease as well as monitor the progress throughout treatment. 

NGS is used in conjunction with liquid biopsies to provide a tumour-specific molecular profile of the cancer. Initially PCR-based methods were used to sequence ctDNA due to their sensitivity and low cost. However, these methods can only screen for known variants, and the input and speed are limited. NGS has shown to provide high throughput and the ability to screen unknown genetic variants. Thanks to NGS technology, the sequencing of ctDNA can be performed at a much higher sensitivity than tissue biopsies and support patients in targeted therapy relative to their tumour profile. 

Microsatellite instability testing

The DNA mismatch repair system (MMR) is a highly conserved repair mechanism for cellular function. However, when the MMR system fails to work properly, it can cause microsatellites. These are regions of repeated DNA that change in length, showing instability.  Lynch syndrome is a common hereditary disease, characterised by mutations in MMR genes. This syndrome is associated with many cancer types, especially so with colon cancer and endometrial cancer.

MSI testing is used to analyse the length of specific DNA microsatellites within a tumour sample in order to measure the level of instability.  

Clinical studies in the last five years suggest that MSI is a predictive biomarker for immunotherapy, seeking further attention for its application in precision medicine. According to a 2019 review, several clinical trials have demonstrated that mismatch repair deficiency or microsatellite instability-high is significantly associated with long-term immunotherapy-related responses and better prognosis in colorectal and noncolorectal malignancies treated with immune checkpoint inhibitors. 

At the time of publication (2019), the drug pembrolizumab has been approved for MMR deficiency/microsatellite instability-high refractory tumours, and nivolumab approved for colorectal cancer patients with MMR deficiency / microsatellite instability-high. The fact that the same biomarker has been used to support immunotherapy for different tumour types is a first in cancer research. This represents a significant step forward in precision oncology, highlighting a promising opportunity to improve the efficacy of immunotherapy.

The three innovations described in this article are a few of the latest developments in precision oncology. As research continues, the hope is that cancer therapy will become more targeted so treatment is more effective and patients receive the best chance of survival.

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Oncology Strategy Meeting, set for 17 June 2021.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post The Latest Developments in Precision Oncology appeared first on Proventa International.

Given the considerable seismic shifts occurring in the pharmaceutical and life sciences sectors recently, it is no surprise that the major trends and investments of 2021 have seen a similar change in focus. Alzheimer’s Disease, genome editing and cellular therapy are three areas which will see increased investment from the pharma industry in the coming months. 

For daily articles on the latest pharma trends and innovations, as well as interviews with leading experts and in-depth industry White Papers, subscribe to PharmaFeatures.com.

R&D: Alzheimers 

Dementia is an umbrella term to describe degenerative neurological disorders which result in significant cognitive impairment that impacts daily life. Symptoms often manifest as problem-solving issues, short-term memory impairment and language difficulties. Alzheimer’s is a specific cause of dementia characterised by early clinical symptoms including depression and difficulty remembering short-term conversations. Late symptoms include confusion, poor judgement, behavioural changes and eventually walking. With an ageing global population, Alzheimer’s presents an unmet clinical condition. As of 2021, there is no cure for Alzheimer’s disease, with only poor symptom management available for patients. 

The current drugs available on the market aim to manage the symptoms of Alzheimer’s, but none can “slow or stop the damage and destruction of neurons that cause Alzheimer’s symptoms”. Psychosis is a particularly difficult symptom that can present in Alzheimer’s with disease progression. Psychosis is a psychiatric symptom in which an individual fails to distinguish fiction from reality. It can include vivid visual and auditory hallucinations. Unfortunately, ”no drugs are specifically approved by the FDA to treat behavioral and psychiatric symptoms”. Psychosis is one of multiple behaviour and psychiatric symptoms that manifest in “almost all patients with dementia in the course of their disease”. 

Aducanumab is the only drug of the five approved by the FDA which shows potential to slow progression of the disease. Hence, a significant amount of time and resources are being funneled into Alzheimer’s research to better understand the pathophysiology. A study published May 15, 2021 a new comprehensive model of Alzheimer’s was published. The longitudinal model is to be used as a prognostic tool which “effectively reproduces the observed course of AD from an initial visit assessment”. The aim of which allows clinicians to project “coordinated developments for individual patients of multiple disease features.” 

Alzheimer’s has recently seen significant investment for novel drug trials in rare forms of the disease. The Alzheimer’s Association, in partnership with the GHR Foundation, has committed $14 million to Tau Next Generation. Tau Next Generation is an expansion of the Inherited Alzheimer Network Trials Unit at Washington University. The aim of this unit is to investigate the efficacy of a class of Azheimer’s drugs known as anti-tau, in individuals with “dominantly inherited Alzheimer’s disease (AD), a rare form of younger‐onset AD caused by inherited genetic mutations.”

Biotech: Genome editing

Genome editing is a powerful tool of genetic engineering in which DNA is inserted, modified, substituted, or replaced within an organism’s genome. According to a 2019 review, the mechanism of genome editing is based on the use of “highly specific and programmable nucleases, which produce specific changes in regions of interest in the genome by introducing double-strand breaks (DSBs) that are later repaired by cellular mechanisms.” 

Nucleases are a broad class of enzymes that cleave nucleic acids. Genome editing has evolved rapidly over the last decade, showing its utility in biotechnology and biomedical research. This method of genetic engineering can be performed in vitro or in vivo in a wide variety of experimental models. The most popular system for genome editing is the CRISPR/Cas9 system. The system performs sequence-specific cleavage through interaction of crRNA by base pairing at the target site. After joining the target site, “the two DNA strands are cleaved by the nuclease domain of Cas9”.

The system has shown significant therapeutic potential for a multitude of diseases. These diseases are primarily caused by some form of known genetic dysfunction. The therapy of genome editing is based on the direct modification of pathological genetic sequences. Whether it be the knock-out of a target gene, introducing a protective mutation, or adding a therapeutic transgene, CRISPR/Cas9 has shown great diversity in its ability to edit the human genome. 

Oncology is one therapeutic area which continues to use genome editing as a therapeutic tool for treating cancer. The current goal of cancer therapy with CRISPR/Cas9 for the coming years “is to remove malignant mutations and replace them with normal DNA sequences.” According to a 2020 Nature article, the screening of functional genes using genome editing is the future direction for precision medicine. The aim is to “reveal changes in gene expression after cancer drug therapy and help to investigate drug-gene interactions by adding small molecules as perturbations”. The hope is that novel targets will be targeted for precise treatment and provide insight into disease development.

Personalised medicine: Cell and gene therapy

Gene therapy is a broad term which covers methods that improve the genetic profile of an organism by “means of the correction of altered (mutated) genes or site-specific modifications that have therapeutic treatment as target”. One of the most recognised gene therapy techniques is recombinant DNA technology. In this technique, a target/healthy gene is inserted into a vector which is then transported into the genome in order to replace an abnormal gene causing a disease. 

Recombinant DNA technology has facilitated many important clinical techniques including genetic screening and clinical technique known as genetic counselling. It appears now that the advances in gene therapy are being applied to “correct inherited genetic disorders such as hemophilia, cystic fibrosis, and familial hypercholesterolemia”. 

In July 2020, a nature article was published suggesting that “gene therapy could offer an inclusive cure for cystic fibrosis”. In the same article, it was highlighted that in October 2019, the Cystic Fibrosis Foundation (US) announced a funding of $500 million “for research into treatments for cystic fibrosis, including gene-therapy approaches”. Over the next six years this function will be used to explore different genetic therapies for cystic fibrosis patients whose condition cannot be treated with existing drugs on the market like Trikafta.Stem cell therapy is an example of a cellular therapeutic technique which has shown potential as a regenerative strategy for a multitude of diseases. In a news release by Yale University, “Yale scientists repair injured spinal cords using patients’ own stem cells”. Stem cell therapy has shown potential over the years to restore nerve damage, up until now however, the results have been less than sold. In the news article, it was stated that additional studies will need to confirm the results of this “preliminary, unblinded trial”, however they remain optimistic. This shows one of many applications for cellular therapies which show potential to treat diseases or injuries not previously thought possible.

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Medicinal Chemistry and Biology Strategy Meeting, set for 29 June 2021

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post Future Trends for Pharma and Life Sciences 2021 appeared first on Proventa International.

Companion diagnostic tests are an important part of precision medicine in oncology. In addition to matching optimal treatment options for patients, these tests enable clinicians to monitor the efficacy of therapeutic intervention in disease management. Innovations in cancer biopsies and blood tests aim to introduce more accurate and less-invasive companion diagnostics.

To discuss these innovations and more with other leading experts in an informal setting, sign up to Proventa’s Oncology Strategy Meeting, held online on 17 June 2021. 

Introduction

According to the National Cancer Institute, companion diagnostic (CDx) tests are used to match appropriate treatment for patients with cancer. The test can identify specific genetic changes in tumours or biomarkers that are targeted by a specific drug. In addition, the tests can be used to determine whether the patient is a candidate for treatment or not. Companion diagnostics are also used to monitor the clinical response to specific drugs which helps to ensure safety and efficacy according to the FDA. 

Companion diagnostics are an important tool for precision medicine in oncology as a strategy to optimise efficacy of patient treatment. Cancer pathology is highly complex: with over 100 variations of the disease and differing progression, personalised medicine is greatly sought after. 

In companion diagnostics, mechanisms in molecular genomics (molecular interactions within the genome) are most associated with pathology, and as such are disease targets. Circulating tumour cells (CTCs) and circulating free tumour DNA (ctDNA) are the two main biomarkers detected by liquid biopsy (the analysis of tumour physiology using biomarkers circulating in bodily fluids). ctDNA is becoming an increasingly popular biomarker for a variety of tumours. 

The presence of ctDNA arises when tumour cells release DNA into surrounding tissue, either via apoptosis (programmed cell death) or active secretion. The ctDNA is then delivered into the bloodstream. Circulating ctDNA is then identified by specific genetic mutations for a tumour type. Quantifying the biomarkers present measures the effect of treatment relative to disease progression, hence is an important part of treatment choice and disease management. 

Barriers to use

The delivery of CDx tests is a topic under debate. While invasive diagnostic tests are potentially more accurate as they involve directly testing a patient’s tissue, they are often unpleasant and sometimes painful procedures. Hence, there is a clinical need to develop less invasive alternatives which are widely available and match the accuracy of invasive techniques. 

Unfortunately, there are a multitude of barriers that prevent CDx tests being used in clinical practice. According to a recent article, inefficient market development planning for CDx tests prevents approximately 50% of cancer patients from receiving the correct test for “biomarker-guided treatment”. Other factors include that the test is not always required, and a sample is not always available. These challenges will need to be addressed to allow the right patients to benefit from some of the new tests that have recently been developed. 

Latest innovations in next-generation techniques 

Blood samples over invasive procedures

In March 2020, the IMvigor011 study reached phase III of an oncology clinical trial using Signatera, a companion diagnostic created by Natera. Natera is a clinical genetic testing company that specialises in cell-free DNA testing technology. Their focus is primarily on women’s health, organ health and oncology. In an article published in early 2020, it describes the randomised clinical trial, sponsored by Genetech Initiate, which was launched to evaluate the safety and efficacy of adjuvant treatment with the PD-L1 inhibitor. Eligible patients are to be screened with Signatera, a companion diagnostic used to identify muscle-invasive urothelial carcinoma. 

Signatera is a customised ctDNA test that monitors treatment and also assesses molecular residual disease (MRD) in patients with previous cancer diagnoses. According to the article, the companion diagnostic requires only a blood sample with blood tests that are personalised to each individual relative to the genetic signature of mutations found in a tumour. 

This is an example of the exciting developments in invasive companion diagnostics techniques which maintains accurate detection of disease as well as creating a procedure better tolerated by patients. This particular diagnostic test has been used to “detect recurrence earlier and to help optimize treatment decisions.” The test awaits FDA approval.

Liquid biopsies and next-generation sequencing

In August 2020, the FDA approved the first liquid biopsy companion diagnostic – Guardant360 CDx assay. According to an FDA press release, this test uses next-generation sequencing (NGS) to recognise patients which present specific mutations of the epidermal growth factor gene. This specific type of mutation is expressed in a deadly form of metastatic non-small lung cancer. It is the first approval for NGS and liquid biopsy in one diagnostic test.

NGS is a DNA-sequencing technique which can sequence the entire human genome in a day. According to a review of NGS platforms and applications, “each of the three billion bases in the human genome is sequenced multiple times, providing high depth to deliver accurate data and an insight into unexpected DNA variation”. In the context of companion diagnostics, NGS is used for high-throughput tumour profiling. In one test, NGS allowed clinicians to detect mutations in 55 tumorous genes, instead of assessing one gene at a time. 

In addition to utilising NGS, the Guardant360 CDx assay also uses liquid biopsy. In comparison to solid biopsies, the procedure is less invasive for patients and can also be repeated easily, which could have a considerable number of clinical applications. 

It appears that companion diagnostics are diversifying. In addition to their application in matching therapy to patients, they are becoming increasingly used to identify the presence of residual disease in order to intervene with treatment before it is incurable. This was a point raised in the 2020 Nature article, in which the author states that “during treatment, regular liquid biopsies could reveal the persistence or increase of CTCs or ctDNA — which would indicate resistance to the chosen therapy. People could then be offered a more effective treatment before the tumour burden becomes excessive and incurable”.

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Oncology Strategy Meeting, set for 17 June 2021.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post Strategies to Optimise Companion Diagnostics in Cancer appeared first on Proventa International.

Despite the prevalence of target-based discovery, phenotypic screening is emerging as a popular technique in early drug discovery. Challenges with target-based delivery have shown obvious flaws with predictive validity. The latest application of AI in phenotypic drug discovery has shown significant potential to optimise current techniques and the drug development process. 

For daily articles on the latest pharma trends and innovations, as well as interviews with leading experts and in-depth industry White Papers, subscribe to PharmaFeatures.com.

In the search for drug candidates, target-based drug discovery (TDD) has been the method of choice for decades. TDD relies on methods with defined molecular targets, enabled by genomics and recombinant technology. In TDD, molecular biological techniques identify the expression of pathological genes which are then tested against a library of large compounds. 

Unfortunately, one of the main concerns with TDD is poor translatability. In other words, targets tested in simple cell-based assays do not present a similar profile in complex organisms, like humans. This particular challenge is suggested to be due to the inability of TDD to comprehend the widespread action of a drug on multiple targets. Selective serotonin  reuptake inhibitors (SSRIs) is a commonly prescribed anxiety medication, and an example of a drug which interacts with many targets, hence a number of side effects that arise. 

Phenotypic screening (PDD) is an alternative method of drug discovery that aims to address some of the challenges with TDD. Phenotype simply means the physical presentation of genetic code (genotype). For example, replication of chromosome 21 (genotype) results in Down syndrome (phenotype). 

Phenotypic assays test drugs within relevant biological systems or pathways to identify active biological compounds. Testing a compound within a biological pathway will reveal any target interactions which will support drug discovery in understanding potential effects of the drug within the human body. This is a point echoed in an article which infers that phenotypic assays aim “to improve the translation of drug discovery to the clinic”. Biological systems within the human body are inherently complex, hence the ability of PDD to test drugs within this environment and allow researchers to potentially identify new targets. 

The fundamental basis for the potential success of PDD is something known as the chain of translatability. This chain of translatability has been described as the “presence of a shared mechanistic basis for the disease model, the assay readout and the biology of the disease in humans”. The significance of this is that it increases the likelihood of strong predictive validity, a key part in predicting the clinical therapeutic response of a drug within humans. 

The chain of translatability is dependent on a deep understanding of the disease at a molecular level in order to best select and validate an experimental cellular system. It is important to identify the pathological molecular mechanisms within a system as this is replicated in vitro for drug screening assays.  

Examples of phenotypic screening 

For PDD at the whole animal level, phenotypic screening is modelled within organisms like fruit flies, zebrafish or mice. This helps researchers to identify the phenotypic changes on a whole body scale that arise from the molecular interaction with drugs. In addition, these in vivo approaches can highlight toxic side effects at very early stages within clinical studies, potentially saving time and money. 

Zebrafish larvae and embryos are a popular choice for phenotypic screening due to the convenience of manipulating the genetic code in high throughput experiments using a method known as automated microscopy. High throughput experiments allow scientists to test vast numbers of molecules, and then profile them against a mass of biological targets in a short timescale. The entire zebrafish genome has been sequenced in its entirety, and so is open to analysis and genomic manipulation. In terms of translatability, >80% of human disease-associated genes have equivalent representation in zebrafish. 

Genetically-modified zebra fish are used to represent human genetic kidney disorders. Kidney disease in humans is a therapeutic area with a relatively solid understanding of pathology, but limited therapeutic options. Hence, zebrafish mimicking the human genetic code for kidney diseases allows researchers to implement chemical screening for potential therapeutic drugs. This phenotype-based, whole-organism screening in zebrafish has a multitude of advantages, one of which enables the identification of potential drug candidates without prior knowledge of a validated target, which TDD relies heavily upon. In addition, it allows the simultaneous assessment “of compound efficacy, toxicity, biodistribution, and pharmacokinetics within a vertebrate model system.”

One possible limitation of PDD over TDD is the sustainability of the discovery pipeline. The extensive process from lead-finding to clinical candidate typically requires greater time and resources in PDD due to the complex screening assays. The development of these challenging assays is one of the causes for a lower probability of technical success in primary screening in comparison to TDD. Other technical risks with phenotypic screening include a high false-positive rate, and often poor results in identifying a molecule suitable for “in vivo proof-of-concept validation”. In other words, a potential drug candidate which was successful in vitro, but does not demonstrate the same effective therapeutic action in in vivo models. 

Latest innovations in phenotypic drug discovery: AI and ML

New computational methods in drug discovery have seen a substantial development in  phenotypic screening. Next-generation phenotypic screening has become an important part of early drug discovery for infectious diseases, introducing deep-learning models already popular in neuroscience. 

Deep-learning models are a class of machine-learning (ML) algorithms that utilise multiple layers to present higher-level features from raw input. One example of an ML-software platform known as HRMAn (Host Response to Microbe Analysis). HRMAn is capable of learning from host pathogens on how to differentiate and assess protein recruitment by host cells during infection. In a 2019 article it states that the “open-source image analysis platform is based on machine-learning algorithms and deep learning, and is highly flexible, as evidenced by its capacity to learn phenotypes from the data without relying on researcher-based assumptions.” This is a prime example of the advantages to using AI-driven approaches in drug discovery. The capacity of HRMAn to classify and quantify pathogenic mechanisms shows potential to enhance early drug discovery for infectious diseases. 

Convolutional neural networks are an example of a deep-learning model in neuroscience. More recently however, it has shown promising applications in oncology. In a 2020 study, a team investigated whether the AI system could classify the “sensitivity of anticancer drugs, based on cell morphology during culture”. The study aimed to determine whether cell features could be classified at a single-cell level using ML. The CNN-based model was constructed to predict the efficacy of antitumor drugs at the single-cell level. ML revealed the model identified the effects of antitumor compounds with an accuracy of 0.8. This is a huge step forward in understanding the potential of computational-based phenotypic screening in precision medicine for oncology.

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Medicinal Chemistry and Biology Strategy Meeting, set for 29 June 2021.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post Phenotypic Screening in Early Drug Discovery: Opportunities and Challenges appeared first on Proventa International.

In a recent interview with Dr. Evangeline Wassmer, Paediatric neurology consultant at Birmingham Children’s Hospital, Proventa discussed the problems with rare disease clinical research. Dr. Wassmer has extensive experience in paedatric rare diseases –  paediatric Multiple Sclerosis, neurometabolic and neurodegenerative in particular. In this article, she addresses the key problems in rare disease research, including study design and factors that impact drug development for paediatric conditions.

To discuss these innovations and more with leading experts in an informal setting, sign up to Proventa’s Clinical Operations and Oncology Strategy Meeting held online on 27 May 2021.

According to Dr. Wassmer: “Both the FDA and EMA have really changed the way they look at things. Drug companies are now allowed to extend their patency for a number of years if they do a paediatric study, so the drug companies are now much more motivated. The patency isn’t just for paediatrics, but for adults too. So you can imagine: for MS drugs which have a huge market in the adult world, an extra five years is a large financial incentive to do a paediatric study. As a result, drug companies are very keen to do these studies now, but there are still problems. 

“For example, the problem with MS drugs is that there are a lot of new drugs in development and the way they design these are akin to in adult studies. To do a conventional randomised controlled study you need a large number of patients. You can reduce the number by using placebo, which is probably unethical because you shouldn’t really not treat patients. However, if you use a placebo you can reduce the number of patients you need to power the study. But if you use a comparative study, you need much larger numbers – so we’re talking about 400 to 500 in each arm as a minimum. If you look internationally, you probably would be able to recruit around 200 paediatric MS patients every year. So as you can imagine, it takes an awful long time for a new drug to do a randomised controlled trial (RCT) – around four or five years.”

Unfortunately randomised controlled clinical trials can be subject to problems in the face of rare diseases including unclear hypotheses, insufficient sample size and poor selection of endpoints.

“One reason a long duration of a RCT is problematic is you never get to see the MRI scans as a clinician, so you don’t know if the drug is working. Also, the patient doesn’t know which drug they are on until at least the end of the study which can take up to five, or six years. So that’s quite hard to be taking a placebo injection for up to five, or six years to know if you are taking the active drug or not.”

Challenges with ultra-rare diseases

The complex issues that arise with rare diseases in clinical research are amplified with ultra-rare diseases. Poor understanding of the pathology and very small patient cohorts make it difficult to test therapeutic treatment of drugs for ultra-rare diseases. We discussed some of these challenges with Dr. Wassmer and their impact on clinical trials. 

“Now there’s a lot of even rarer diseases in which there are very few patients in the country, or even the world. So, those much rarer diseases are harder [to recruit for]. Firstly, these rarer diseases don’t have treatment, so any treatment you are using or trying out, you can pretty much only do phase I or phase II studies – so safety studies; and then in a small group you can try and test some efficacy. It is very hard to get to do a feasible phase III or even randomised studies because the numbers are just too small. What they often do are natural cohort studies. So, run a baseline and then compare the treated patients to the natural history. There are a lot of natural history studies going on. It’s a shame we didn’t start collecting that data years ago in anticipation for treatments, but for natural history studies you really must have long-term outcome data, not just two years.”

The impact of poor FDA/EMA collaboration 

Achieving FDA and EMA approval is the final goal for drug development programmes. Unfortunately, the differing regulations between the two bodies often result in problems for rare disease research. 

“Ideally you would have a surrogate biomarker, like an MRI scan or a blood marker, but the FDA prefers clinical outcome. The EMA is more amenable to using surrogate biomarkers.”

Unfortunately, drug companies are now creating two different drug designs – one for the FDA and one for the EMA. Which is a disaster really, because we then only have small numbers of patients as there are two studies which divide the small numbers. For example, in paediatric MS,  a drug company had three different study designs with the same drug and that’s a headache, because that means you can only recruit a very small number of people. This means that all three studies are set up to fail. 

“We did a meeting around five years ago, where we got the EMA and FDA in the same room and also included people from the university in Birmingham who discussed potential study designs. In terms of study designs, oncologists have done this much better. They have developed their network and multiple treatment options in factorial designs. The people from the university discussed  adaptive designs that aimed to reduce the number of patients you need. This needs further work and discussions with FDA and EMA.”

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Clinical Operations and Oncology Strategy Meeting, set for 27 May 2021.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post The Clinical Challenges of Rare Disease Research: An Interview with Dr. Evangeline Wassmer appeared first on Proventa International.