The COVID-19 pandemic created a significant shortage of suitable patients for clinical trials worldwide. Within this crisis was another more serious issue around inclusion and representation, driven by COVID’s disparate effect on certain ethnicities, that spoke to a wider issue of certain patient populations being unable to find or be invited into important trials for them. Proventa spoke to Liz Beatty, Chief Strategy Officer at Inato, about how they believe this issue can be rectified. 

Proventa International: Tell me about yourself, and how Inato was founded?

Liz Beatty: I’m CSO at Inato, and have been with the company now for around two years. Before that I was at Bristol Myers Squibb for 16 years in various roles, including clinical operations. When they closed our office in 2018, I decided to go out and see if I could help shape and influence new technology coming into the clinical trial space. 

I joined Inato in the early days of the company, and was well aligned with it on technology’s impact on accelerating drugs to market, helping patients get access to new medicines faster. We’ve since been growing our position as a company that can help all investigators offer the right trials to their patients. 

Inato was founded in 2016 as a feasibility platform for sites and sponsors, running feasibility questionnaires. During this work we heard a lot from smaller sites that they weren’t getting the same opportunities as larger academic centers and didn’t know how to position themselves properly, and heard a lot from sponsors that traditional feasibility models weren’t providing them with the site performance they needed.

Because of this, we believed there was an opportunity to do something different to bring access to smaller sites, while showing reliability to sponsors. In 2020, we halted our feasibility operations and launched our current marketplace model.

Inato’s goal is to make trials accessible to patients, as well as ensure sites and physicians are aware of the trials going on and that they have access to them for their patients. We’re also hoping to ensure trials are inclusive by going to a broader range of sites, as opposed to just the top few that are used most by pharma. 

We think there is an opportunity to be more inclusive in research, and to ensure that underrepresented populations are part of trial datasets. Finally, we want to make all this work more efficiently by accelerating drug development and reducing anything that slows down timelines in clinical trials. 

PI: Why is it so important to engage underrepresented populations in clinical trials?

LB: Historically, clinical trials have been largely performed within Caucasian populations, and even historically taking male participants over female. It’s a very important problem we’re facing as an industry. Even from a broader perspective, in the United States, diversity and inclusion are part of a big discussion around all aspects of our culture and society. 

We need to take this opportunity now to leave no-one out as an industry, and ensure data is representative. It’s the right thing to do from an ethical perspective, but for trial data it’s also important that all patients are represented to treat diseases that affect different populations unequally. 

If our data doesn’t represent populations accurately, then when a drug goes to market, we won’t have reliable data on how effective or safe it is in those patient populations. This is important to ensure treatments are safe and effective for all patients with that disease, not just certain subsets that were in a clinical trial. 

PI: What are the best ways to enroll these communities?

LB: It’s important to realise that participating in a clinical trial is a big decision for a person to make. Trust is an important factor that comes with that. People have trust in their local communities, in the nurses and doctors that they see day to day. The best way to ensure underrepresented populations are better included in trials and medical data is to ensure trials are available near them, with doctors and medical staff that they already have a relationship with. 

This trust in medical providers can really make or break a patient’s decision on whether they want to participate in a clinical trial. Ensuring that local, community sites have access to more trials for the patients can do this.

When Inato started out, many companies were relying heavily on data and data-driven models to support site selection. But there needs to be a combination of data and a current assessment of what the site’s interests, needs and capacities are for a given trial. For this, sites need to be able to raise their hands and self-identify about what opportunities are best for them and their patients. 

PI: Are there any risks specific to partnering with more community-based trial sites? How can these be overcome? 

LB: Pharma companies are certainly very reticent to partner with sites they’ve never worked with before. They don’t know the staff, how the site is organised and run, or how it has performed in the past. This creates a lot of risk, because it’s a brand new working relationship. Even operationally, you need to create new contracts, budgets – it’s all new in such a situation.

At Inato, we try to understand the ins and outs of the sites we work with in the community – their timelines, experience, past performance – all these characteristics that would help position them for an upcoming trial.

We support them all through this process, ensuring they’re overcoming initial challenges which might occur with a new sponsor. Acting as an intermediary between site and sponsor to understand timelines and performance and capabilities helps reduce risk of working with sites on operations. 

In effect, it’s about finding the strengths of individual sites and working to those. This is very important for diversity and inclusion: just because a site is in a diverse community doesn’t mean they actually serve those patients right now. To that end, it’s good to characterise access to patients to a very specific level with sites, in order to understand which patients they’re seeing at their centre, who they’ve enrolled in the past, strategies used to engage with the community, etc. 

It’s a very specific job for every site. You can really help shape capabilities, which patients they serve, and bring them forward to the right trial for them. 

PI: Over the COVID-19 pandemic, there was a shift to decentralisation and a push towards wearable and disruptive technologies, providing more patient data. Will this new data improve representation? Or simply provide more information on current trial populations?

LB: I think it depends on the indication. For some of them, the data you’re collecting could be quite broad and remote. For others, patients would still need to come to the clinic for tests. Additionally, the role of individual doctors treating patients could vary. 

Decentralised trials will overall help sites participate more, as that shift to more flexible systems and greater technological support will definitely generate a shift in clinical trials to improve access in their communities. 

That said, I think the role of the investigator is still very important in many disease areas, and we must ensure broader access to more investigators in the community to allow underrepresented communities to engage better.

PI: Will the investigator role change post-pandemic? 

LB: Investigators still have an important role, and over COVID many have adapted well to performing telemedicine visits and having remote calls with patients. Personally, I hope we don’t revert from the changes that have been made. I think more trials need to be brought to the patient, and the improvement of technology has really helped. COVID-19 has inspired a lot of change in this area. I hope as an industry we continue to leverage technology to bring trials to the patient. 

Often, the reason patients don’t participate is economic, for example not being able to take time off work, problems with childcare, etcetera. What’s happening during COVID should help patients have more flexibility in how they participate, and bring a broader range to trials. 

Joshua Neil, Editor
PharmaFeatures

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The post Raising Representation in Clinical Trials: An Interview with Liz Beatty, Inato appeared first on Proventa International.

The disparity between animal models and clinical efficacy is compromising the safety and efficacy of therapeutic drugs. Translational research is building the bridge between preclinical studies and therapeutic treatment. Genomic techniques and non-traditional animal models are examples of translational approaches supporting drug development. 

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Introduction to translational research

Across the scope of scientific research there are two main categories known as basic and applied research. Basic research is performed to gain a greater understanding of newly discovered molecules/cells, unusual phenomena, or processes with a limited understanding. The goal is to further scientific knowledge without an obvious or immediate benefit. 

Applied research is the practical application of science focusing on the development of technology and techniques for future problems. To clarify, an example of basic science would be a preclinical model of diabetes, whereas applied science would be a doctor administering a drug to control a patient’s blood sugar. 

One of the main problems across the pharmaceutical industry is the translation from basic science to applied science. Animal models are a fundamental part of basic scientific research, however they are not always able to encapsulate human physiology. The complexity of the human body and disease presents a challenge in creating translatable preclinical animal models.

Translatability is a critical factor in understanding disease pathology for target identification as well as predicting the pharmacokinetics of a drug candidate in humans. Suboptimal trial design, poor patient selection and a lack of validated disease biomarkers are a few examples of the challenges that arise with poor translation from basic to applied science. 

The goal of translational research is to integrate advancements in preclinical studies with clinical trials to take research from the “bench-to-bedside”. The translational approach to drug development incorporates “the target of a specific unmet clinical need from the outset; targets mechanisms underlying clinically relevant problems and designs drugs to address those issues directly”. 

Reverse translation 

In order to address the current dilemma, the concept of reverse translation has been developed. In reverse translational research, data from human subjects is used to develop new hypotheses for testing in the lab and develop new animal models and therapeutics.

A number of new technologies created in the last few years have supported the implementation of reverse translational research. 

GWAS studies 

GWAS (genome-wide association studies) are an example of a genomic application used to support reverse translation. This has been used to identify “hundreds of novel genes and gene variants associated with risk or protection against human diseases”. The identification of disease-associated genes supports the creation of animal models which more accurately reflect the development of the disease. This enables the exploration of newly identified genes and novel disease pathways to support target identification and validation in drug discovery. 

Recently, a 2021 study performed GWAS on autoimmune Addison’s Disease (AD) – a complex inherited disease and the most common cause of primary adrenal failure in the Western world. If untreated, the disease is fatal. Previously, the small patient population has limited the success of gene studies. Utilising the two largest Addison’s disease biobanks in the world, this study conducted the first GWAS study for autoimmune AD which revealed “nine genome-wide significant associations and explained 35-41% of the additive genetic heritability”. 

The nine risk loci, in addition to the four novel loci, achieved genome-wide significance for association with the disease which is a significant step forward in understanding the correlation of autoimmunity in this disease. Loci refers to the “specific physical location of a gene or other DNA sequence on a chromosome”.

The significance of this reverse translational approach enables the creation of animal models for the study of rare, inherited diseases of which there may have been none previously available due to genomic complexity. 

Molecular profiling 

Molecular profiling is another approach to reverse translation used to “identify patterns of RNA and protein resistance associated with disease resistance”. Next generation sequencing (NGS) is another genomic tool as a part of molecular profiling. NGS enables the “simultaneous analysis of a broad spectrum of genomic alterations, including mutations and copy number variations (CNV)”. In comparison to conventional techniques which require unique assays for each analyte, NGS provides a more cost-efficient approach for tissue analysis. 

This approach to genomic analysis has become increasingly popular in precision oncology.The genetic and protein-based profiling of tumours has begun to better define the molecular signatures of tumour types. The historical challenges with cancer treatment has been noted as the ‘one size fits all’ approach to treatment across the spectrum of cancer types, potentially due to the poor translation from the challenge of developing preclinical models. 

However, now, the genomic profiling of cancer types using tools like NGS can optimise specific genomic targets for drug development and predict treatment response. As a result, molecular profiling of tumours has been associated with better responses to immunotherapy and higher patient survival rates. 

Non-rodent models 

Rodent models remain at the forefront of preclinical studies for a number of diseases, however smaller animal models have become increasingly popular over the last few years. Small animal models such as fruit flies and zebrafish are a cheaper, faster alternative to rodent models for disease modelling and can “provide a bridge between in vitro and rodent models”. 

In addition to the feasibility of small animal models, they can still offer the same scientific value as rodent models. In zebrafish, over 80% of human disease genes are conserved and the transparency of cells enables fluorescent labelling which can be visualised in situ over time. 

There are a number of additional advantages these small animal models offer over traditional animals models which supports translational research:

• Reverse genetic screens to identity new molecular partners of disease genes and new drug targets

• High-throughput phenotype-based screening 

• Enhancer/suppressor screens for drug discovery 

The therapeutic benefit of these smaller animal models has been seen in drug development across many fields. A number of approved cancer drugs including crizotinib and vandetanib were developed in fruit fly models. 

Continuous development of translational approaches will further optimise the drug development process and produce therapeutic treatment which is safer and more effective for even the most complex of diseases.

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 Bioinformatics Strategy Meeting, set for 1 July 2021.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post Using Translational Research to Bridge the Gap Between Preclinical and Clinical Research appeared first on Proventa International.

The General Practice Data for Planning and Research (GPDPR) is a proposed central NHS digital database collating patient data from GP records in England. Originally 1 July 2021, the date for launch has been pushed back to 1 September after the NHS called for a delay to allow patients to learn more about the system and consent regulations. 

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Introduction 

The system is based on the collection of data from GP practices “so that it can be used for health and social care purposes including policy, planning, commissioning, public health and research”.

According to the BBC the collected data will include sex, ethnicity, sexual orientation, diagnoses, medications and information about a patients’ physical, mental and sexual health.

Patient data collection from GPs is needed to support everything from scientific research to healthcare settings and services. One example is analysis of national medical data which can often reveal trends within certain populations, linking specific diseases to gender/ethnicity and other factors. This links with one of the areas the government hopes to utilise GPDPR for, specifically researching the long-term health impact of COVID-19 on the population. 

Another target for the system is analysing data for healthcare inequalities. For example, understanding “how people of different ethnicities access healthcare and how the outcomes of particular groups compare to the rest of the population”. 

Research and development is another area which the NHS digital platform aims to support. For example, analysis of data from cancer patients may reveal a trend between type and family history, which is useful in directing genomic cancer research.

For this digital database, NHS digital will not collect names, addresses, written notes, documents, and coded data that GPs are not permitted to share by law (e.g. IVF).  

Concerns regarding implementation 

Despite assurance from the government regarding data use, there are obvious concerns surrounding data privacy and security. 

Firstly, many patients do not wish for sensitive data like mental illness or sexual orientation to be shared outside of the appropriate practices. Fears about employment implication and social stigma are a few of the concerns with the sharing of such data. The Guardian commented on this particular point inferring that the identification of individuals from medical records is still feasible even with limited data. 

Secondly, there are a number of concerns regarding data security due to the concept of a centralised datastore. In comparison to systems spread out over a number of platforms, a central datastore is more vulnerable to cyber attacks. The obvious impact of this is potential sharing of data with third parties for profit.

Recent applications of healthcare data

Identifying disparities in care

According to the BBC, GP data has been used to identify disparities in care for individuals with learning disabilities. This is an example of the positive impact analysing healthcare data can have for tackling inequality in healthcare settings. 

A 2016 public health report details the findings of GP data collection from this patient population, and the potential impact it could have with regards to improving access to healthcare. 

Based on estimates from collected data, the report suggested that “only 23% of adults with learning disabilities in England are identified as such on GP registers, the most comprehensive identification source within health or social services in England. The remaining 77% have been referred to as the ‘hidden majority’ of adults with learning disabilities who typically remain invisible in data collections used in this publication”. 

Furthermore, in comparison to “expected death rates in the general population, there were 2.8 times the expected number of deaths from circulatory diseases and 4.9 times the number of deaths from respiratory diseases in individuals with learning difficulties”. Preventable deaths like epilepsy also contributed to 3.9% of the death statistics.

It highlights an obvious flaw in the healthcare system, whereby vulnerable populations are not receiving the appropriate care and attention they require. Without collection and analysis of GP data, it would not only be harder to be aware of the inequalities but it also supports governing bodies in addressing these challenges. 

COVID-19

More recently, healthcare data has proven to be a useful tool in the fight against COVID-19. The University of Oxford RECOVERY clinical trial uses patient data to “identify treatments that may be beneficial for people hospitalised with suspected or confirmed COVID-19”.

During this trial, a range of potential treatments including corticosteroids were tested for improvement in the condition of patients hospitalised with the virus. The results from this trial, i.e. patient data, are continuously collected, analysed and compared against each other to assess the success of each treatment type. 

In addition to drug efficacy, data analysis may reveal whether a specific form of treatment is potentially more beneficial for certain ethnic groups of patients with specific pre-existing health conditions. 

Patient data is also important in this context in terms of identifying whether specific sub-groups of the population are more likely to be hospitalised from COVID-19. A 2021 study recently revealed how COVID-19 has disproportionately affected minority ethnic populations in the UK. 

The observational cohort study analysed the electronic health records of adults registered with primary care practices “for whom electronic health records were available through the OpenSAFELY platform”.

In general, the results highlighted that minority ethnic groups in the UK have had disproportionately high levels of poor COVID-19 outcomes. This focused on the risk of COVID-19-related hospitalisation, ICU admission, and death after accounting for clinical comorbidities in England.

Capturing healthcare data seen for this study has enabled healthcare professionals to gain real-time insights into the ethnic disparities across different severities of COVID-19 infection. This data is an important part of improving the quality of, and access to, care for minority ethnic groups across England and the UK in general. 

While the aforementioned studies support the collection of medical data, it is important to take into account patient concerns regarding the upcoming ‘NHS data grab’ with regards to cybersecurity, data protection, and data privacy. Addressing these concerns remains an important part of this shift in healthcare data use, and further engagement with the public about this is another area for improvement. 

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post Understanding the GP Data Extraction Scheme & Use of Patient Data appeared first on Proventa International.

As a part of Proventa’s Clinical Operations and Oncology Meeting May 2021, we organised a number of interesting keynote sessions pondering the latest innovations, challenges and strategies for the future. The morning keynote, hosted by Tanya Russel Kirkpatrick, VP Clinical Operations Head – Oncology, Global Product Development at Pfizer, was a particularly fascinating panel discussion focused on how the COVID-19 pandemic has impacted oncology and clinical operations.

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The industry has also seen a significant shift in terms of how it operates clinical trials, focusing on delivering a more patient-centric approach to research and healthcare. There is a “general emphasis on home patient care, remote monitoring and essentially making these trials accessible to patients”. What has been obvious is patients’ concern regarding clinic visits and the risk of catching COVID-19. “In some instances we saw patients who could/could not enroll in trials simply because they did not feel comfortable meeting face-to-face or perhaps because they were part of a vulnerable patient subset or simply it was logistically impossible to do so”.

Clinical trial diversity and equity was something [positive] that came out from the pandemic in particular. “For a long time [as an industry] we have not had representative patient populations enrolling in our oncology trials, and certain indications, for example prostate cancer and triple negative breast cancer”.

Also noteworthy were some differences in how big pharma reacted to the pandemic versus the reaction of smaller biotechs.

Small biotech companies

Despite the world being thrown into turmoil, pharmaceutical startups were able to conduct remote monitoring and patient care almost immediately. Moving quickly into action mode, these smaller pharma companies worked with vendor partners to make sure that the first and foremost, the patient, were not impacted in terms of safety. This is a prime example of the shift to focusing more on patient-centricity.

Start-ups were able to quickly pivot to a substantial amount of remote monitoring, with guidance received from global regulatory bodies like the FDA, which helped these companies to rapidly achieve this. One speaker was happy to report that “none of our patients missed a visit: we worked with our vendors to facilitate patient travel. In oncology, it’s always going to be difficult with infusions and ensuring the patients are comfortable, keeping in mind the patients’ desires and perspectives – all this feeds into our decision-making”. 

Remote technologies 

Across the clinical field, sites immediately implemented remote technologies, allowing doctors to continue to conduct telehealth visits with their patients. Sponsors partnered with sites would work to figure out which visits would be in-person vs those that could be done remotely at a local lab or imaging centre (for oncology).

During the pandemic, a large number of cancer patients were concerned about personal visits, hence were afraid to receive or visit for cancer screenings which further emphasised the need for more telehealth to be implemented. In terms of oncology, one speaker suggested that the status of clinical trials today is hybrid, “where there’ll be some visits closer to home, some at the centre, and some closer to the patient’s”.

There also appears to be a perception that it is more difficult to run decentralised clinical trials for oncology, however one speaker reinforced “it’s all about enabling home health like sending clinicians to patients’ homes to either draw blood, administer medications, collect safety, just checking in general on the patient.”

With regards to remote engagement, “the general feedback from telehealth [from sites] is that they think the patients like it better; it helps to engage and actually ask more questions when they’re doing telehealth and maybe the stress of coming into the office”. 

One of the challenges found with home health , however, is that some patients are uncomfortable having people come into their homes – this is one of the things which pharma is working on improving. 

Initially vendors had no control over which home health visits were meeting patients, so patients were seeing different people each time which was making some uncomfortable. It’s important to establish a relationship between the home health visitor and the patients they’re visiting in order to build a bond of trust.

“It’s all about offering a choice to patients, keeping them at the centre of the decision making”. 

Big pharma

Big pharma initially experienced a high number of [oncology] trials being put on hold. From a Pfizer perspective “we went through the whole pausing trials: we kept open some of the oncology trials, but most of our trials globally were paused briefly because of site shutdowns and then reopened”.

Reopening was a huge effort: site reopening was based on their ability to allow patients on site, their ability to monitor, and also whatever the local shutdown regulations were. Doing this globally was one of the most difficult things to do: “we had our colleagues in countries reporting back on a regular basis on sites regarding their status and ability to allow patients to come into the clinics/not coming to the clinics”. 

Oncology trials were an area that reopened faster than some of the other therapeutic areas because often oncology sites have a separate location – it’s not in the main hospital when they’re treating the patients they’re administering the oncology treatments too. Big pharma’s very quickly adapted to both remote monitoring, set up for working with sites to see how data could be accessed throughout electronic health records. The varying levels of lockdowns globally presented an issue however, as Europe wasn’t allowing that type of monitoring early on, whereas in the US you could get better data from sites depending on the region 

“Because we [Pfizer] developed the vaccine, we quickly pivoted to asking how can we do that in the pandemic. It enabled us to kickstart the things we wanted to do for a long time. We wanted to have a more decentralised approach”. This enabled (remote) monitoring, and thus, more telehealth. Due to the pandemic, big pharma had to adapt to remote clinical research to enable the vaccine trials to run. The success of the rapid changes to decentralised clinical trials has pushed big pharma to continue in this way and make sure they were conducting clinical trials before – this is one of the biggest challenges.

Supplying medication to patients’ homes was “one of the first things we implemented…but you’re less able to do that in oncology”. In oncology, it was often more about trying to figure out how to get the patient into the centre for infusions or something that required monitoring for safety. It was found that oncology centres were more willing to open up because they wanted to push continuity of patient treatment. “Direct-to-patient drug delivery and flexible sample collection is something we’ve been doing – collecting bloods, sending phlebotomists to homes and sending them back to central labs and even local labs”.

“What we’re doing for decentralised trials is more of a concierge approach, so every time we have a new protocol, the protocol team sits down with the decentralised subject matter experts. These are professionals who are dedicated to their subject, looking visit-by-visit to see what’s happening and make suggestions where they could deploy a technique that might be more beneficial for the patient”. 

This typically results in a hybrid approach, where the first couple visits involve travel to the hospital to monitor patients for cytokine release syndrome (CRS) or other safety issues. Visits after that could be done at home.

In oncology, there are often long-term follow ups, with patients on the drug for a long period, receiving benefits. This can go on for years and makes it easier to perform visits from a home-type approach.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post How Has The Covid-19 Pandemic Impacted Oncology and Clinical Operations? 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.

Risk-based monitoring (RBM) is a useful tool in maximising the quality of clinical trials. Enhanced site communication, greater patient safety and lower costs are a few of the benefits of RBM. The innovations of AI in clinical research have application in RBM, optimising further the design and conduct of clinical trials. 

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RBM allows sponsors to identify and address potential issues that could comprise the quality of a clinical trial. Identifying, assessing, monitoring and minimising any potential risks is of paramount importance to prevent delay or termination of clinical trials. 

There are three key parts of RBM:

• Identify critical data and processes

• Risk assessment

• Monitoring plan

According to FDA guidance, the risk assessment serves to “identify and understand the nature, sources, likelihood of detection, and potential causes of risks that could affect the collection of critical data or performance of critical processes”. The more information about high-risk areas for potential issues or critical data points, the easier a monitoring plan can be developed. In the FDA document, there are a number of points raised when determining the type of monitoring required. The complexity of study design; study endpoints; clinical complexity of study population; and geographic location of sites are four of the nine factors that need to be considered for RBM. 

In addition to the obvious identification of a risk and its origin, re-education of the site and amendment to recruitment plans are other ways in which a risk assessment can be performed. 

In order to identify the critical data points at the start of the clinical trial, it is important to identify the variables that need to be measured to answer the original scientific question of the study. Critical data points in clinical trials can include anything from the target parameters, to compliance for subject criteria to serious adverse events. 

A white paper by IQVIA highlights a number of statistics in support of RBM for clinical trial efficiency, including “developed RBM solutions can bring as much as 25% cost reduction over traditional trial execution approaches”.

Application of AI in RBM

Predictive analytics

Predictive analytics is one of the more recent innovations in RBM that incorporates AI technology. It is the practice of extracting information from existing data in order to determine patterns and predict future outcomes and trends. In terms of risk management, it can be used to predict future scenarios with an acceptable level of reliability, in addition to ‘what-if’ scenarios which can be used to develop a risk assessment. 

Machine learning (ML) is an important part of clinical predictive analytics. Due to the accessibility of electronic health records (EHR), the volume of data in clinical research has increased substantially. Therefore, computational software has become widely used as a more cost- and time-efficient method of analysing vast amounts of data.

In 2018, a retrospective, single-site study investigated the use of ML to identify high-risk surgical patients using automatically curated electronic health record data (Pythia). The first aim of the study was to develop a ML model that could analyse high-volume, high-quality data (to monitor care and patient outcome). The second was to promote the development of ML models that can be used to interpret clinical data, and support clinicians in identifying potential high-risk patients. 

Random forest and extreme gradient boosted decision trees were a few of the ML methods used to predict the likelihood of post-surgical complications. In addition, a method known as Lasso penalized logistic regression was used due to the ability of the algorithms to quantify the importance of variables in a dataset. The study emphasised that “by providing model users with additional information about predictor weights, clinicians can glean insights into potential patient risk mitigation strategies”. 

The positive results demonstrated the efficacy of utilising ML for predictive analytics. The 42 models used demonstrated “strong predictive performance”, with the random forests showing the greater. The prediction of high-risk patients in this study reinforces the translatability to RBM in clinical research. Using predictive analytics could help forecast potential issues which can be addressed and alter risk assessments accordingly. 

AI Data Security

Thanks to the introduction of digital innovations like telehealth, the amount of data within clinical research is increasing substantially. Furthermore, the pandemic has seen a rapid rise in the number of decentralised clinical trials globally. This new era of virtual clinical research has brought a number of advantages, streamlining clinical trials with cloud-based software. However, with confidential data like EHRs being shared across multiple platforms, data security has become a priority within RBM. 

Blockchain is an example of technology which has converged with AI and is becoming increasingly popular for data security within clinical trials. The traditional centralised server models are vulnerable to the single-point attack limitations and malicious insider attacks. However, blockchain technology spreads data across a large network of databases in replica copies, rather than a single store. The benefit of this is that stored data is less vulnerable to hacking or infringement. Furthermore, the incorporated verification ensures data is protected from unauthorised access. In terms of the convergence with AI, blockchain can provide privacy and trustworthiness, and AI utilises machine learning algorithms on the blockchain to achieve security, scalability and effectiveness. 

It is worth noting however, blockchain technology still has some limitations. Expensive software, large storage and high bandwidth are a few of the challenges with blockchain which may be not suitable for the smaller pharmaceutical companies and contract research organisations. 

With the help of AI, RBM will continue to evolve at the same pace as the digitalisation of clinical research. The value of predictive analytics has reinforced the importance of developed models to enhance RBM for clinical trials, reducing the time and cost spent addressing issues that could have been predicted earlier.

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.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post Innovations in AI Risk-Based Monitoring in Clinical Research appeared first on Proventa International.

The modernisation of clinical research is an important part of expediting the drug approval process, and this in turn is dependent on the success of clinical trials with a sufficient number of appropriate participants. A number of recent innovations promise to bring about this modernisation: for example, using artificial intelligence (AI) software to research medical databases for eligible participants reduces the time scale for recruitment. Telemedicine will encourage more patients to take part in clinical trials from the comfort of their own home. But as considerable a solution as they are, these new technologies do have their own challenges to face.

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Telemedicine and virtual clinical trials

Telemedicine is the use of remote technology, typically for clinical consultations and the delivery of healthcare. It enables communication between patients and healthcare providers outside the clinical environment. One example of telemedicine is the recording of vital signs during the sleep cycle using actigraphy bracelets, for example Fitbit. 

Telemedicine has facilitated clinical researchers in capturing and analysing data remotely. This method has been desirable for both patients and clinical organisations. Firstly, limited travelling to clinics is more likely to increase patient retention in clinical trials, allowing them to continue day-to-day life. Secondly, the data is more likely to be representative of real-word evidence, in comparison to clinics which do not always show a patient’s true behaviour in an unknown, clinical environment. 

Telemedicine has shown obvious potential in clinical trials for rare diseases. Rare diseases typically have small populations across the entire globe, hence a central site for clinical trials may not be accessible. In March 2017, the European Reference Networks (ERNs) were launched “as virtual networks enabling healthcare providers across Europe to access and share expertise for the care of patients with complex or rare disorders.” 

According to a 2017 publication, the ERN is composed of “at least ten healthcare providers from at least eight different Member States”. In addition to addressing the small number of patients, it enables the liaison of medical experts across the world. This offers an advantage for rare diseases, a therapeutic area which typically lacks knowledge of disease pathology and therapeutic options. 

The increasing number of virtual trials that emerged during the pandemic utilised telemedicine to continue clinical trials, reducing patient-clinician contact in COVID-19 risk assessments. In comparison to traditional clinical trials, patient recruitment, consent and data collection all occur virtually. Face-to-face clinical appointments are often eliminated altogether, along with physical sites. 

The development of virtual trials, also known as decentralised clinical trials, demonstrates the industry evolving rapidly to “deliver approaches that reduce patient burden, increase patient engagement, and promote trial continuity”. The success of virtual clinical trials throughout the pandemic demonstrated the benefits of a patient-centric approach to clinical research. 

One of the challenges to overcome with the digitalisation of clinical trials is the skepticism surrounding data security. The majority of clinical data is highly confidential, with data sources like electronic health records containing a patient’s entire medical history. 

Blockchain is the latest advancement in data security within clinical research. Blockchain technology is a shared system for recording transactions, tracking assets and building trust in a network. The benefit of this is that stored data is less vulnerable to hacking or infringement. Furthermore, the system involves verification steps which ensures the data is protected against unauthorised intervention. 

Artificial intelligence in patient recruitment

Patient recruitment is one of the many areas in clinical trial design streamlined by AI. Natural language processing (NLP) is a branch of AI that enables computers to analyse the written and spoken word. In the context of medicine, it has been used to “allow algorithms to search doctors’ notes and pathology reports for people who would be eligible to participate in a given clinical trial.” In a report by Nature, it is suggested that refinement of NLP could be used in clinical trials to search patient databases for eligible participants. The inclusion and exclusion criteria of clinical trials is typically written in plain text, so shouldn’t require complex algorithms like those required to analyse doctors’ notes.   

In addition to patient recruitment, AI models can also be used to enhance cohort selection. Electronic phenotyping is a well-established discipline which focuses on “reducing population heterogeneity, namely the process of identifying patients with specific characteristics of interest.” Using electronic medical records, “individuals with an explicit observable trait from large quantities of imperfect clinical patient data” can be identified, also known as phenotyping. 

This method is primarily used to reduce patient population heterogeneity rather than enhancing the quality of prognoses. Despite this, phenotyping with EMR data presents a number of challenges including “variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels”.

There are a number of other challenges that exist at present. Firstly, a challenge for the pharmaceutical industry is the lack of personnel to operate AI/ML-based platforms. Furthermore, there is often skepticism about the quality of data generated by AI. Small organisations are often limited in their budget so cannot afford to invest in AI/ML technology. 

Cloud-based software

An optimal clinical trial management system (CTMS) is an important project management tool that supports pharmaceutical and clinical operations. A CTMS maintains contract and payment systems, document management, study milestones and contact management for sites and teams. Legacy CTMS’ are essentially outdated systems within clinical trial management. Recent events highlight the benefits of virtual clinical trials, but current CTMS’ will struggle to manage increasing amounts of data from many different platforms. 

Because of this, the industry is moving towards Cloud-based platforms. Cloud-based applications offer a unified clinical platform to collate documents and data, direct sharing of information with external partners and being able to configure a CTMS system to any type of clinical trial.

Innovations in AI and modern technology are going a long way to address some of the issues of traditional clinical research, including poor patient recruitment. While these innovations do come with their own hurdles to overcome, it is without doubt that in the future they will be refined to improve further.

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.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post The Impact of Modern Technology on Clinical Operations appeared first on Proventa International.

Real-world data (RWD) and real-word evidence (RWE) are increasingly used to support drug development and clinical research across life sciences. RWE studies provide insight into the implementation of therapeutic drugs clinical practice based on RWD of the patient population. RWD and RWE have shown significant value in supporting the regulatory decisions in both the drug development process and healthcare settings.

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What are real-world evidence and real-world data?

As defined by the FDA, RWE is “the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD”. RWD can arise from a variety of sources measuring the status of patient health and/or the delivery of therapeutic care. Electronic health records, disease registries and patient-generated data (in home settings) are examples of platforms that record such data. It is important that RWD is captured in a natural, non-interventional manner rather than clinical trial settings. This ensures that the patient/healthcare data captured is representative of real-life circumstances.

Originally, data collected from randomised controlled clinical trials (RCTs) was considered “higher than that in the real world” according to a 2018 review. Randomisation and double-blind protocols in these trials ensure that comparable cohorts are formed. 

Unfortunately, there are some limitations to RCTs. One obvious limitation is the exclusion and inclusion criteria, which exclude a proportion of patients that may be eligible in the real world. This introduces generalisability, and in turn a level of uncertainty about the efficacy of the drug for two reasons:

• As it is only being tested in a specific group of the patient population, the clinical therapeutic response in other patients could vary greatly  

• Factors such as low compliance and reduced tolerability in the clinical setting may not be truly representative of the drugs “performance” in the real-world. 

As a result, RWE has become an important part of evaluating the efficacy of treatments. In the review, it is suggested that data from RCTs could be supplemented with RWD to bridge the gap between the controlled clinical setting of RCTs and the “harsh realities” of the real world.

RWE and RWD: Drug development

RWD and RWE play an important role in FDA regulatory decisions. According to the FDA article, both RWD and RWE support the organisation in monitoring post-market safety and potential adverse effects of therapeutic drugs and devices.This will have a substantial impact on the drug approval process, by understanding the efficacy of treatment in clinical research and real-world patient care. 

In the FDA article, it was also reiterated that “medical product developers are using RWD and RWE to support clinical trial designs (e.g., large simple trials, pragmatic clinical trials) and observational studies to generate innovative, new treatment approaches.” The importance of RWE studies in drug development is emphasised in an article by IQVIA. It is highlighted that drug developers can use RWE in pre-launch studies as insights in selecting clinical trial endpoints and optimising their recruiting strategies.

In clinical pharmacology, RWD has been useful in solving many issues including the optimisation of dose and regimen. The dose and dose regimen at the time of drug approval should be associated with an acceptable benefit-to-risk profile. In some cases, however, pharmacologists question whether these two factors are optimal i.e., could a higher dose potentially improve therapeutic efficacy without a considerable increase in toxicities. This was a particular point emphasised in a 2019 article reviewing the use of RWD and RWE in drug development. In the article, it is suggested that RWD can be used to identify whether modifications in dosing within the real‐world setting are “performed as recommended in the product labeling, potentially providing information on the clinical outcome (both safety and effectiveness) when clinical practice differs.”

RWE and RWD: Clinical research

These pre-launch (RWE) studies are particularly valuable in the advancement of rare disease therapy. A placebo trial arm may be considered unethical and impractical in the small patient populations of rare diseases. In cases like this, RWE studies can “fill the gaps by providing comparator arms and answering preliminary questions about the treatment journey”.

In oncology clinical trials, due to a limited number of patients it is the common side effects of anticancer drugs that are revealed. The more toxic adverse events however, may be missed due to the limited diversity of patients as a result of restrictive inclusion/exclusion criteria. Limited follow-up duration is also suggested to blame for poor estimation of risk associated with adverse events. 

RWD has been suggested to play an important role in addressing these issues, by providing better characterisation of tolerability and adverse events. An accurate toxicity profile for anticancer drugs is critical to inform physicians and patients about the safety of treatment. This was a key point raised in a 2020 review, which suggested that RWD could be a useful “establishing a definitive analysis of benefits and risks associated with treatment for clinical practice guidelines”.

The healthcare community also uses these data to support decision-making and development of guidelines for use of treatment in clinical practice.

Potential challenges

In comparison to clinical trials, RWD is gathered from a multitude of sources, so it is important that the relevant information is captured. Data access and quality are two of the challenges when using RWD, as highlighted in a 2019 article. The article raises the fact that RWD can come from many types of databases, including pharmacy dispensing data or electronic health records (EHR). The data often varies in quality and can include missing data. Pharmacy dispensing records, for example, are not always the most reliable. Even if a prescription is filled for a patient, there is no data to confirm they are taking as prescribed or even taking a drug at all. Over-the-counter drugs are often not documented in RWD. This introduces a potential confounding factor that can comprise the reliability of RWD.

Integrating data from the numerous RWD sources is also a challenge. Combining data from different platforms including EHRs, digital health devices and genomic imaging is a difficult task. As suggested in the aforementioned article, technological solutions like Natural Language Processing could integrate the data to create a more comprehensive picture and novel insights. 

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.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post Real World Data and Evidence: Emerging Trends Across Clinical Research appeared first on Proventa International.

Targeted therapy – a treatment targeting proteins responsible for cancer growth – is one of the latest developments in precision oncology. The variable response of cancer treatments highlights the need to develop therapies that can target the specific profile of different tumours. Small molecule and antibody-drug conjugates are the next step in reaching this milestone.

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Precision oncology largely focuses on identifying drug candidates that will effectively treat a tumour, followed by a prescribed treatment regimen. This is critical in developing therapeutic options for patient populations which do not necessarily benefit from conventional treatment like chemotherapy. Chemoresistance and lack of selectivity for tumour sites, for instance, can negatively impact a patient’s response to conventional cancer treatment. Currently in precision cancer treatment, molecularly-targeted drugs are based upon the genomic properties of the drug-target gene. This approach, however, only benefits a small proportion of patients.

Targeted therapy is emerging as an effective strategy in precision medicine for cancer treatment. According to an article by the National Cancer Institute, these drugs work by blocking and interfering with molecular targets involved in the growth, progression and spread of the disease. The majority of targeted cancer therapies are either small molecules or monoclonal antibodies (manufactured proteins that mimic human antibodies).

Antibody drug conjugates (ADCs) 

The increasing use of immunotherapy in cancer emphasises the advantages of using antibodies against tumours. In comparison to small molecules, they offer greater selectivity towards antigen-presenting cells, which reduce off-target toxicity and have a longer half-life. Unfortunately, the limited number of therapeutic antibodies commercially available for cancer treatment is small – which, according to an article, highlights “the difficulty to identify antibodies with clinical efficacy as single agents”.

In order to enhance the selectivity and efficacy of tumour-killing cells, monoclonal antibodies have been combined with cytotoxic compounds (potent anti-cancer agents) to form ADCs. The antibody is connected to the cytotoxic compound by a linker. This creates a drug delivery system to the antigen-expressing cells in the tumour. The binding of the antibody to these cells causes a mechanism known as ‘internalisation’, before inducing the release of the antitumor cytotoxic compound.

 According to a recent source, ADCs date back more than 50 years, and have achieved great success in recent years with the introduction of clinically approved drugs such as Brentuximab vedotin. This drug was approved by the FDA in 2011 based on the assessment of two-phase II clinical studies. The first study showed the “objective response was 75%, with 34% complete remission”. The second found that “86% of the patients achieved an objective response with 53% CRs”. The drug is approved in over 65 countries for improving clinical outcome for patients with relapsed/refractory anaplastic large cell lymphoma (ALCL) or relapsed/refractory classical Hodgkin lymphoma. 

One of the main advantages for using ADCs is the high specificity and affinity for target antigen-presenting cells. This is critical in cancer treatment to ensure toxic mechanisms do not occur in healthy tissue.

It is essential the payload remains intact before reaching the tumour in order to avoid damaging healthy tissue. Unfortunately, if the linkers in the ADCs are not stable enough, the cytotoxic payload can be released prematurely, which risks killing healthy cells. This was a problem with first generation ADCs which caused severe side effects with a limited therapeutic window.

 Other issues include “low efficiency, low internalization, off-target effect due to the target expression in normal tissues”. These are the main areas of focus for clinical research to improve the efficacy and safety of ADCs.

Small molecule drug conjugates 

Small molecule drug conjugates (SMDCs) are a new, less established perspective for targeted therapy. According to a 2019 review, SMDCs are made up of a targeting ligand, a spacer (connection between ligand and bridge), a cleavable bridge and a therapeutic payload. The payload is released after infiltrating the tumour cells. Cytotoxic substances, chemotherapy for example, are typically the therapeutic payloads which cause cell damage or death inside the tumour. The review emphasises three main criteria the payload must meet three criteria: “(1) have a high efficiency of releasing the therapeutic payload, (2) cause fewer multidrug interactions and less intracellular metabolism after its release, and most importantly, (3) have a high binding affinity”.

The ability of SMDCs to deliver potent cytotoxic agents directly into the tumour microenvironment is one of the reasons why they show potential for targeted therapy. In addition, their non-immunogenic profile in comparison to other therapies (ADC) reduces the chance of an autoimmune response. According to a source, the synthesis of SMDCs is also relatively convenient, and typically they have better cell permeability. The independent units that make up SMDCs structure, can be substituted until an optimal drug candidate is identified. The capacity to be manipulated suggests that SMDCs could potentially have a positive impact on drug discovery in finding optimal conjugates. 

In terms of success, clinical trials investigating the SMDC PEN-866 have shown significant results. PEN-866 is an inhibitor which aims to block HSP90, a protein which is often overexpressed in solid tumours of cancers in the breast, pancreas, and lungs. PEN-866 targets the protein and releases the cytotoxic payload. The first in-human study of PEN-866 was in 2017, assessing the safety, tolerability, pharmacokinetics, and preliminary efficacy of the drug conjugate. All participants involved were diagnosed with progressive, advanced solid malignancies. 

For a phase 1 study, the results were generally positive stating that “PEN-866 was well tolerated and demonstrated preliminary evidence of antitumor activity. PEN-866 will be evaluated in Phase 2a expansion cohorts enrolling multiple solid tumours”. In the context of drug development, this is a positive first step forward in identifying a potential drug candidate for target cancer therapy.

Unfortunately, there remain a number of issues with SMDCs in targeted cancer therapies. According to a 2021 article, the currently available SMDCs used in chemotherapy suffer from “poor bioavailability, rapid blood/renal clearance, nonspecific selectivity, low accumulation in tumours, severe multidrug resistance (MDR) and adverse side effects related to healthy tissues.” All of these issues are significant as they directly impact the efficacy of the drug long-term and patient quality of life. Poor bioavailability and low accumulation in tumours are especially an issue as it infers the pharmacokinetics of the drug conjugates have not been fully investigated or optimised.

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 15 May 2021.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post Precision Medicine in Oncology: A Comparative Evaluation of SMDCs and ADCs 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.