The field of clinical trials and pharmacovigilance (PV) is constantly evolving, driven by advances in medical science, technology, and regulatory requirements. As we look ahead to the next five years, it is evident that significant changes and innovations will shape the landscape of these crucial areas. In this article, we will explore the upcoming advancements in clinical trials and PV that are set to revolutionize the healthcare industry.

Leveraging Artificial Intelligence (AI) and Machine Learning in Clinical Trials

In the coming years, AI and machine learning will play a pivotal role in transforming the way clinical trials are conducted. These technologies have the potential to streamline various processes, enhance data analysis, and improve patient recruitment. AI-powered algorithms can quickly identify suitable candidates for specific trials based on their medical history, genetic makeup, and other relevant factors. This efficiency will lead to reduced costs and faster trial completion, ultimately benefiting patients and researchers alike.

Decentralized Clinical Trials (DCTs) and Virtual Trials

Traditional clinical trials often involve the physical presence of participants at specific research sites. However, DCTs and virtual trials are gaining momentum, especially after the COVID-19 pandemic accelerated the adoption of remote healthcare solutions. With DCTs, patients can participate from the comfort of their homes, eliminating the need for frequent travel and reducing logistical complexities. This approach increases patient diversity and expands the reach of clinical trials, providing a more representative sample for research purposes.

Personalized Medicine and Targeted Therapies

The era of personalized medicine is dawning upon us. Advancements in genomics and molecular diagnostics have opened doors to targeted therapies tailored to an individual’s specific genetic makeup and disease characteristics. This approach can lead to more effective treatments with fewer side effects, as medications are optimized for each patient’s unique biology. As we progress, personalized medicine will become increasingly common in clinical trials and drug development, revolutionizing patient care.

Real-World Evidence (RWE) and Post-Marketing Surveillance

While clinical trials provide valuable data, real-world evidence (RWE) is gaining importance in assessing a drug’s performance in the broader population. RWE encompasses data from electronic health records, claims databases, and patient registries, offering insights into long-term safety and effectiveness. Regulatory agencies are increasingly relying on RWE to support post-marketing surveillance, which will influence drug approvals and label updates in the future.

Blockchain Technology for Data Security and Integrity

The decentralized and immutable nature of blockchain technology makes it an ideal candidate for enhancing data security and integrity in clinical trials. By using blockchain, researchers can ensure that sensitive patient data remains confidential and unaltered, reducing the risk of data breaches and fraud. Additionally, smart contracts on the blockchain can automate various trial processes, making them more efficient and transparent.

Wearable Devices and Digital Endpoints

Wearable devices and digital endpoints are revolutionizing how clinical trials collect data. These devices can continuously monitor patients’ vital signs, activity levels, and other health-related metrics in real-time. By providing a constant stream of data, wearable devices enable researchers to have a deeper understanding of a drug’s effects on patients over an extended period. This data-rich environment will lead to more informed decision-making and better treatment outcomes.

Regulatory Evolution and Streamlined Approvals

Regulatory agencies worldwide are recognizing the need to streamline drug approvals and reduce time-to-market for critical medications. Consequently, they are adopting innovative approaches to expedite the approval process while ensuring safety and efficacy. Such changes will encourage pharmaceutical companies to invest in research and development confidently, knowing that they can bring life-saving drugs to patients more efficiently.

Patient-Centricity and Inclusive Trial Design

Patient-centricity will continue to be a driving force in shaping the future of clinical trials. Researchers are placing a greater emphasis on involving patients in trial design and decision-making processes. By understanding patients’ needs and preferences, researchers can design trials that are more inclusive and better aligned with real-world scenarios. This approach leads to higher patient engagement, improved retention, and ultimately, more reliable trial results.

Advanced Data Analytics and Predictive Modeling

Data analytics will become even more sophisticated, allowing researchers to derive valuable insights from vast amounts of data generated during clinical trials. Predictive modeling can help identify potential safety issues early in the development process, preventing adverse events and facilitating better risk management. Furthermore, advanced analytics can aid in patient recruitment, ensuring that trials enroll the right candidates efficiently.

Global Collaboration and Data Sharing

Collaboration among researchers, pharmaceutical companies, and regulatory authorities across borders is vital to address global health challenges effectively. By sharing data and knowledge, stakeholders can collectively work towards developing innovative treatments for rare diseases and unmet medical needs. Initiatives like data-sharing platforms and international research consortia will foster a more interconnected and collaborative research ecosystem.

In conclusion, the future of clinical trials and pharmacovigilance is bright and promising. The integration of cutting-edge technologies, patient-centric approaches, and streamlined regulatory processes will propel medical research to new heights. Embracing these future trends will not only accelerate drug development but also improve patient outcomes and contribute to advancing healthcare on a global scale.

The post Future Trends in Clinical Trials and Pharmacovigilance for the Next 5 Years appeared first on Proventa International.

Dose prediction is a critical part of drug discovery which determines the safety and efficacy of drugs within patients. Dose response predictions are made with preclinical models which quantify pharmacokinetic (PK) behaviour via a number of parameters. Despite the continuous use of in vitro and in vivo models, it appears that automated, machine learning approaches are leading the way in the latest innovations of human dose prediction. 

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.

Introduction

There are two main factors linked to dose prediction: PK and drug-drug interactions. Pharmacokinetics is the behaviour of a drug within a system, and includes bioavailability, distribution, metabolism and excretion. Drug-drug interactions (DDI) can occur when multiple drugs are co-administered to a patient. DDI is an important part of drug development, as it can lead to altered systemic exposure, causing variable drug responses. Typically DDIs occur due to inhibition of the metabolism for one drug by the other; it leads to a rise in plasma concentration of the drug whose metabolism is inhibited.  

Preclinical data helps to inform of the likelihood of achieving the desired therapeutic dose in early clinical development. From here, the prediction methods are assessed and further refined from the comparison of clinical data with the original predictions. Due to continuous developments in predictive methodologies and data analysis, the probability of successful PK predictions has increased over the past few years. According to a 2020 study, 83% of AstraZeneca drug development projects progress in the clinic with no PK issues. 

The higher the probability of successful predictive models, the faster the progression from drug discovery to market. Poor prediction of drug behaviour in clinical trials can significantly hinder drug development, either due to poor efficacy or toxicity issues. In terms of safety testing, drugs are often tested in concentration-response assays. The results are quantified by an XC50 value, which is a measure of the potency of the compound, and which lies between the lowest and highest concentrations tested. 

Potency is a key component of predicting PK and is a measure of drug activity relative to the amount of drug required to produce a desired effect. Another important assessment is the maximum concentration of a compound when it reaches the target, known as Cmax. This refers to the clinical dose, and compounds given at higher doses tend to have a greater risk of toxic side effects, making Cmax one of the best predictors of clinical toxicity. 

Efficacy of current models

Through a combination of in silico, in vitro and in vivo assays, there are four PK parameters a drug must pass in order to inform about its pharmacokinetic activity for dose prediction.

Prediction of volume distribution (Vd) is a measure of the relative affinity of a drug for tissues and plasma. Vd is reasonably predicted from in silico and in vitro modelling. Using preclinical in vivo PK data, typically animal models, Vd is the most predictable of the PK parameters. 

The prediction of drug clearance (CL) uses a combination of animal data and human in vitro modelling. The primary CL routes in humans and preclinical species are hepatic metabolism and renal and biliary elimination, and methods are required to predict these parallel elimination processes in humans. Identifying the elimination route and rate in preclinical species relative to human CL makes this one of the most challenging parameters in drug discovery. 

Allometry is a method used to predict drug CL and is still widely used within the industry. According to a 2016 study, allometric scaling is where the “exchange of drug dose is based on normalization of dose to body surface area”. In other words, it is used to scale physiological rates relative to the growth and size of one (animal) species relative to another. 

Prediction of bioavailability is another key parameter and refers to the extent and rate at which a drug accesses the target site. Poor bioavailability comprises the efficacy of a drug if the desired therapeutic dose is not correct once the drug reaches its target. In addition, low bioavailability can create issues in obtaining appropriate exposures in pharmacodynamic (PD), efficacy, and safety experiments for a candidate drug. 

Finally, the prediction of pulmonary absorption is an important part of optimising drugs delivered directly to the lungs. The two main strategies for optimising drug retention in the lungs after inhalation involves maintaining a drug reservoir within the airways and a slow rate of dissolution. 

Innovations in predicting human dose

The industry has seen continuous developments in in vitro/vivo/silico models in order to optimise human dose production. However it appears that computational biology and AI are leading the way in the latest innovations. 

In immunology, a 2021 study used machine learning (ML) attempts for predicting human subcutaneous bioavailability of monoclonal antibodies. The aim of this study was to develop a ML approach to determining whether the bioavailability of monoclonal antibodies was >70%. The measured bioavailability of the monoclonal antibodies ranged from 35% to 90%. The team used multiple ML methods including random forest and decision trees. These tree-based methods proved to best predict bioavailability. 

Since all of the ML approaches used theoretical calculations and predictions for input, it was suggested from the study that these models may be most useful for early-stage activities like molecule formational design. 

Oncology is another therapeutic area which has utilised automated methods for dose prediction. Intensity modulated radiation therapy is a deep learning approach which delivers specific doses to target tissue while sparing adjacent organs at risk. This is an important part of cancer treatment, by helping to reduce radiation-related toxicities. 

In 2020, a study investigated fully automated dose prediction using generative adversarial networks in prostate cancer patients. The preliminary study focused on CTs of prostate cancer patients using a generative adversarial network framework, the choice of which was suggested to be due to the “variations of the targets in the prostate cancer patients [being] relatively small”. 

The future for ML methods is suggested to optimise treatment strategy before radiation, as radiotherapy can cause complications with organ placement and re-irradiation.

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 How Effective is the Automation and Validating of Human Dose Predictive Models? appeared first on Proventa International.

Failure to reach the desired safety profile is one of the main reasons for delayed drug development. Advancements in preclinical studies have seen the optimisation of in vitro assays to better predict toxicity in drug discovery. Computational methods like in silico modelling show potential to introduce more rapid and cost-efficient toxicity screening, and address some of the limitations of current in vitro techniques. 

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.

Toxicity is a measure of any undesirable or adverse effect of chemicals. The chemical toxicity of a drug is a critical part of FDA approval which determines whether the therapeutic effect outweighs potential adverse effects. Toxicity failures of drug candidates are a concern in the pharmaceutical industry as it poses a threat to patient health, and also increases the overall cost of the drug development process. 

According to a 2018 article, there are six main classes that need to be tested to predict drug toxicity:

• Irritation and corrosion

• Respiratory toxicity

• Endocrine disruption

• Acute oral toxicity

• Cardiotoxicity 

• Hepatotoxicity

Hepatotoxicity is damage to the liver caused by exposure to chemical substances. It is the leading cause of drug failure in drug discovery. The mechanism by which the liver breaks down substances is complicated and varies between patients, hence hepatotoxicity is considered difficult to detect in preclinical and clinical trials. 

Endocrine (hormonal system) disruption is also a concern, especially for drugs that interact with nuclear receptors including estrogen and androgen receptors. Nuclear receptors are a class of proteins responsible for sensing steroid and thyroid hormone among other specific molecules. Chemicals which disrupt this system could interfere with the normal functioning of these steroid hormones, which could lead to adverse health problems like reproductive disorders. 

Current methods predicting drug toxicity

In vitro bioassays are the primary method by which drug toxicity is measured. In vitro toxicology examines the toxic properties of drugs in cells or tissues maintained in controlled lab conditions.  Metabolic conversion is an important feature these assays require, as toxicity often arises through metabolic activity. In a 2018 review, traditional toxicity bioassays were said to be “often inaccurate when accounting for reactive metabolites”. High-throughput screening (HTS) and high content analysis (HCA) are now the most popular methods for toxicity drug assessment. 

HTS platforms use cell-based assays which measure biological processes such as cell death/growth, protein expression and receptor binding. The output from HTS provides data to make a decision about drug design and generate lead compounds with desired physicochemical properties for the target therapeutic effect. 

These assays are important in preclinical studies to prevent a drug from going further through drug development, should the safety profile not meet the appropriate parameters. This reduces the cost of drug discovery, preventing further preclinical studies, and also reduces the need for animal models.

Other HTS platforms use cell-free assays which can still study biochemical activity. In a 2020 Nature article, it is highlighted that “both formats use a variety of detection technologies including fluorescence and luminescence readouts.” A 2018 study used a combination of organotypic culture models and HTS assay to evaluate chemical toxicity to a full extent but in a more rapid and cost-effective manner. 

An organotypic culture is resected tissue from an organ which preserves the architecture and cellular interaction for further experimentation. This study in particular investigated the toxicant response to multicellular human or rodent liver cells. Their rationale for this decision was to address the challenge of identifying hepatotoxicity in a model which more closely resembles in vivo interactions. 

Despite the success with HTS and HCA, the data set from toxicology studies using these methods is so large and complex, it can become a difficult task to process using available data management tools. This was a point emphasised in a 2014 review, which emphasised it is important to develop “a big data approach” for processing these studies. 

More evolved data management tools will help to collate the data extracted into a response profile. This is an important part of toxicology studies which gives detailed information about the drug’s interaction with relevant biological proteins. Automatic data processing was highlighted as a recent method “developed to identify hits (i.e., actives or toxic compounds) from quality-assured HTS data and to increase the data dissemination and reproducibility”. 

Novel data mining was another method that was suggested to be developed in order to extract useful data from various resources. Data mining refers to a process by which specific trends or patterns are identified in large data sets. This is usually facilitated by software which identifies patterns in large datasets. 

In silico prediction of chemical toxicity

In silico bioassays are an important tool needed to “predict and limit interferent compounds being misinterpreted in fluorescent assay technologies”.  In comparison to in vitro assays, computational methods are more rapid, cost-efficient, environmentally sustainable and can be performed before a compound is synthesised. Structural alerts (SAs) are an example of a type of in silico modelling. According to a review, these are chemical structures which indicate or associate with toxicity. SAs are useful in drug design in terms of how compounds can be altered to reduce their toxicity. 

Using SAs to predict toxicity allows pharmacologists to identify the structure of potential metabolites implicated in mechanisms that lead to toxicity. Unfortunately, there are a number of limitations highlighted in the aforementioned review. SAs do not provide “insights into the biological pathways of toxicity and may not be sufficient for predicting toxicity”. This is of particular concern as it limits the ability to identify other potential targets a drug may interact with that could lead to adverse effects. Furthermore, “the list of SAs and rules may be incomplete, which may cause a large number of false negatives (i.e., toxic chemicals predicted as non‐toxic) in predictions”.

Another one of the main challenges with computational modelling is that the majority of mathematical models are constructed to simulate the physiology of an averaged cell population. This presents an issue as individual-cell interactions can vary greatly from the average cell population. 

Computational methods like in silico modelling have the potential to revolutionise drug discovery, toxicity studies especially. By reducing the need for animal models which are expensive and time consuming, the hope is that computational biology will accelerate the drug development process.

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 Predicting Drug Toxicity: Challenges and Innovations appeared first on Proventa International.

Off the back of its successful 2020 Strategy Meetings for Clinical Trials and PV experts, Proventa asked its leading facilitators how their fields would change over the next five years:

COVID-19 and Safety (Remote Monitoring)

One delegate, a chief science officer at a leading company, noted that safety issues are some of the most important today. They noted that even after the vaccines for the current pandemic are approved, the situation will never be as it was before. Because of this, they advocated remote clinical monitoring as an important aspect of clinical studies right now, due to vitally increasing safety and increasing clinical trial efficiency.

Another delegate spoken to advocated for increased remote trials. They noted that both telemedicine and remote visits are gaining greater importance and relevance right now, and that it would be likely they persist beyond that given their effectiveness and convenience, and that hopefully the coming years will see more user-friendly technologies patients can use for such decentralised visits.

Despite the importance of safety in the post-COVID world, they also noted that speed for roll-out of new COVID-19 vaccines was something suffering from a certain degree of hype at present.

Efficiencies and Costs

The CSO noted the importance of increasing efficiencies in the near future, as there is an increasing sense of urgency to get new drugs to market.

A director of clinical operations, on the other hand, thought that while there are always new technologies for streamlining data, merging information and creating other efficiencies, they would not soon be revolutionising the field: “Trials have largely run the same for years and due to the complex nature of trials globally. Any shift from the normal paradigm is highly unlikely- it’s just too risky, and many in the industry and at the site level are hesitant to stray too far from what they know.” They did, however, note costs as a major problem today, and one which would hopefully improve in the future.

AI

The CFO pointed out AI as one of the most promising aspects of current drug development: “Digital integration of clinical trial data can feed into the trend that may revolutionise new medicines”. A further delegate agreed with this, noting that “AI will be revolutionary”. They said that apps, on the other hand, have yet to live up to their promise, and could well be an example of industry hype.

Patient Recruitment/Identification

An expert attending the event noted that patient recruitment and identification are some of the most important aspects of clinical trials right now, as one of the biggest challenges facing professionals. This would continue to be the case over the next five years, they thought, though potentially with the trend towards less burdensome trials this could eventually be solved.

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 Development Strategy Meetings in Princeton and San Diego, set for 18 and 27 May 2021 respectively.

Joshua Neil, Editor
Proventa International

The post A Look Ahead: Clinical Trials and PV Over the Next 5 Years appeared first on Proventa International.

This year, Proventa debuted its successful online event format for the first time, providing experts from across the sector with an online forum just as useful and educational as our physical events have always been. During the recent Clinical Trials/Pharmacovigilance events, held on 25 June, delegates received the chance to not only discover new insights in their chosen profession but also make important connections and engage with peers through the interactive roundtable discussions and panel debates.

After the event, several facilitators relayed what they thought the main obstacles and themes of the next five years will be. These ranged from the problem of COVID-19 and safety, to how AI will change clinical trials and pharmacovigilance over the coming years.

If you’re unable to see the below PDF, click here.

For more information on future events, including the upcoming Clinical Operations, CTSC and Pharmacovigilance meetings in November 2020, click here.

Joshua Neil, Editor
Proventa International

The post The Future of Clinical Trials and Pharmacovigilance appeared first on Proventa International.

What is Antimicrobial Resistance?

Antimicrobial, or antibiotic, resistance is the ability of micro-organisms such as bacteria to resist the effects of antibiotic medication. It generally evolves through natural selection or selective pressure, and can be transferred between bacteria by plasmid exchange. 

This evolution can come about in a number of different ways. These include overuse of later-generation antibiotics; wrong diagnoses and treatments; improper use of antibiotics by patients; or the use of antibiotic medication in livestock as a growth promoter, prevalent particularly in China.

One of the most well-known examples of antibiotic-resistant bacteria is MRSA, a “superbug” (i.e. carrying several resistant genes). It is endemic in hospitals as well as a number of urban regions around the world. The illness causes fast-acting and deadly diseases such as necrotizing pneumonia, severe sepsis and necrotizing fasciitis. Other resistant bacteria, such as Streptococcus pneumoniae and Enterococcus faecium, are becoming more resistant to antibiotics 

The fallout of such resistance is considerable. Beyond the threat of illnesses such as pneumonia, gonorrhoea and tuberculosis becoming conventionally untreatable, antibiotic resistance will in turn lead to longer stays in hospitals for patients, increased strain on medical services and a rising mortality rate.. 

Current Actions

Since coming to the fore of global consciousness in 2014, the response to antimicrobial resistance has risen significantly. In 2015 the WHO created a global plan to improve awareness and reduce incidents of resistant infections. The plan would also optimise use of antibiotic medicines and encouraging sustainable investment in new drugs to kill resistant bacteria. 

The following year, the UN General Assembly signed a declaration to support a co-ordinated approach, looking particularly at human and animal health and agriculture. Global national action plans have been made, and several initiatives are being led to address the concern. These include: 

• The World Antibiotic Awareness Week, held each November
• The Global Antimicrobial Resistance Surveillance System, a standardised approach to collecting, analysing and sharing data on resistance to improve decision-making and government action
• The Global Antibiotic Research and Development Partnership between WHO and the Drugs for Neglected Diseases initiative to encourage R&D through partnerships, hoping by 2023 to deliver four new treatments

Beyond the UN and WHO, the US Centers for Disease Control and Prevention (CDC) have launched their own Antibiotic Resistance Solutions Initiative. This contributes to national action plans and enhances lab capacity to report resistance.

Individual companies and charities have created other projects, like the Pew Charitable Trusts’ Shared Platform for Antibiotic Research and Knowledge (SPARK). This is a Cloud-based virtual lab to encourage sharing of data and insights into antimicrobial bacteria. SPARK also analyses and integrates biological and chemical data from studies. 

What is Pharma Doing?

The U.K’s Association of the British Pharmaceutical Industry (ABPI) argued that pharma is “fighting a war on five fronts.” These are:

• Reducing unnecessary use of antibiotics – including by training healthcare professionals on antibiotics use and removing volume-based incentives for sales teams
• Ensuring bacteria are not overly exposed to antimicrobials on site. MSD committed $100 million to infrastructure upgrades, ensuring factory discharges contain no harmful residuals
• Developing new antibiotics – including an NHS-partnered ‘subscription-style’ payment model to incentivise new antibiotics R&D
• Slowing infection spread by maximising uptake of vaccines and ensuring hospitals accord with prevention controls 
• Fighting global antibiotic resistance – Pfizer, for instance, noted its launch of the Antimicrobial Testing Leadership and Surveillance website, one of the largest such surveillance programs in the world. 

Despite these efforts, according to WHO the sector that can make the biggest difference is not doing enough. Pharmaceutical companies are creating “insufficient [development efforts] to tackle the challenge of increasing emergence and spread of antimicrobial resistance.”

It said those drugs being developed by big pharma only offer limited improvements on drugs already in the market. According to its report, the majority of innovations are instead being made by small or medium-sized pharma companies, though these have only eight new antibacterial drugs have been created since 2017 – with “limited clinical benefits.”

This is compounded by the pressures placed on smaller pharma organisations, which are often insurmountable: antibiotic frontrunner Achaogen made news last year after filing for bankruptcy, losing dozens of pipeline drugs and its FDA-approved antibiotic plazomicin, effective in treating infections caused by superbug Enterobacteriaceae. 

The WHO cited sector concerns around the current lack of treatment for gram-negative bacteria, which can cause pneumonia, bloodstream infections and meningitis. 

Is Pharma Doing Enough?

Assessing the pipeline alone, it would seem that the pharma community really is pushing for action against these concerns: 32 of the 50 antibiotics currently in pipelines will address pathogens described as ‘global priorities’. Of these, three target New Delhi metallo-beta-lactamase 1, a dangerous enzyme which makes bacteria resistant to a wide range of antibiotics, including one commonly seen as the ‘last line of defence’ against antimicrobial-resistant infections. 

This is not the whole truth, however. A number of companies, such as Novartis in July 2018, are actively leaving the field, two years after it set out to combat the problem. Merck, Roche, GSK and Pfizer are some of the few remaining pharma giants still involved in the sector. 

A second WHO report, looking at preclinical drugs in development, was more promising than the first: it found that 252 of those currently under investigation could potentially target ‘global priority’ bacteria, though scientists have yet to make safety and efficacy tests on many of these. Most will also require a decade to bring to market.

Despite the limited attention focused on the area, some benefits have been seen. The drug pretomanid, developed by the not-for-profit TB Alliance, has been approved for patients with drug-resistant tuberculosis. 

The WHO’s director, Hanan Balkhy, certainly thought more could be done. While he noted that a number of initiatives were under way, pharma companies must contribute “with…innovative new medicines.” 

Discussion from the sector almost unanimously agrees however that the issue is more complex than “not trying hard enough”. It comes down to profit, and the lack of it in the antibiotics field. Sales of new antibiotics are a fraction of the return from other drug classes. Efforts to increase profit, such as extended patent lifetimes and proposals to guarantee minimum sales, have largely failed to reverse the trend. 

Possible Answers to the Problem

Following Achaogen’s collapse, a number of groups including the Pew Charitable Trusts and Infectious Diseases Society of America wrote to Congress in February 2019, proposing a series of measures to push forward development of new antibiotics. These measures included tax incentives for pharma companies, ‘pull’ incentives that increase new antibiotics’ value and changes in pharmaceutical reimbursement – for example a $1 billion market entry reward over five years for companies that receive regulatory approval for an antibiotic that addresses a specified public health need. 

Last year saw the Developing an Innovative Strategy for Antimicrobial Resistant Microorganisms (DISARM) Act of 2019 introduced in the U.S. This would alter Medicare to reimburse hospitals who treat patients with anti-microbial infections by removing disincentives for using new antibiotics. 

Experts have discussed changing the payment model for antibiotics. Antibiotics don’t fit into the current model due to the short duration of patient use, limiting the drugs sold. The need to limit antibiotics prescribed or sold also reduces pharma sales. 

This model change is doubly necessary due to the pressure antibiotics will face from competitors. Most classes of antibiotic have lost exclusivity. This means that companies can produce generic alternatives to undercut the hard work of big pharma. 

Any significant research into the area of antimicrobial resistance makes two things abundantly clear. The first is the necessity of big pharma stepping up their efforts in antibiotic R&D and pipeline activities. The second is the fact that unless major changes are made to the way such drugs are priced in the market, this will never happen. Beyond charitable organisations, big pharma is first and foremost a money-making enterprise, one that needs a constant stream of financial remuneration to operate. Unless new antibiotics become a viable, successful business decision, the dream of a new wave of antibiotics to fight resistance is simply impractical. 

Joshua Neil, Editor
Proventa International

The post Antibiotic Resistance: Is Pharma Doing Enough? appeared first on Proventa International.

The Need for Innovation

Pharmacovigilance is one of the most important aspects of drug development, with the potential to have a more direct and life-changing impact on patients than any other area. Adverse drug reactions (ADRs) account for around 7% of hospital admissions in the US alone, with around half of these entirely avoidable. 

The situation is becoming critical. Between 2004 and 2015, there was a five-times increase in reported ADRs, with the EMA noting that in 2015 there was a 10% rise in ADRs on the previous year. Beyond being a danger to patients, the rise in ADRs increases costs for pharma PV departments and increases workload for staff dealing with such matters. 

Even today, a considerable proportion of the work done by PV professionals is manual. The majority of PV spend in most pharma companies is focussed on processing rather than analysis, with huge amounts of resources going towards the use of manual workflow models to ensure enough data is captured and stored correctly that analysis can even begin at all. 

The Benefits of AI

The ability of AI to help PV professionals is self-evident. Computers’ increased ability to parse and compile huge reams of scientific data is allowing for more and more complex calculations and reports to be made without human aid. The automated creation of audit process models allows for more detailed and accurate data collection and analysis, with fewer mistakes or gaps than might be the case with human effort. 

Beyond this, with the increased intelligence and speed of AI programs, data can be mined from hundreds of sources that are as yet untapped: social media, emails and scientific literature are all phenomenal resources to identify ADRs, unlocked by the promise of AI and Natural Language Processing – within standards of privacy and necessity, of course. 

AI and ML also have the benefit of being able to enact a culture of transparency and openness that will better encourage trust in the general public. This in turn will create a more positive reflection of the pharmaceutical industry, with demonstrations of the hard work and accurate analysis that goes into saving patient lives and reducing ADR rates. 

A perfect example of this is the work Bayer has been doing in its PV section. Working with AI company Genpact, Bayer has added smart algorithms to its existing PV systems. The AI solution extracts ADR data from numerous documents, streamlining data processing and reducing the possibility of error. 

A recent study by Pfizer employees examined the utility of AI in AER case processing. Looking at three different solutions, the study determined that AI processes could not only successfully extract meaningful data from AER documents, but also use safety database data fields as a quicker and cheaper alternative to annotating source documents manually.

The Necessity of Caution

Despite this promise, the industry should be cautious when implementing AI and ML solutions into its processes. Any AI used should be well-validated and certain of precision and data security. 

Likewise, the human element will still to a certain point still be constantly involved in the process. Automation will undoubtedly reduce almost all low-skilled or monotonous labour from the lives of pharma professionals, as it will in every field, from law to surgery. But to the same extent it will act as an enhancer of the human element of any work, freeing up man-power and time for higher intensity and more complex work, and – largely in the case of complex analysis and decision-making – assisting humans as they determine the right course of action. 

Other issues require a light touch on automation and AI. Data privacy is a constant concern – and full automation leaves every step of a process open to hacking or data leaks, which in the absence of human observation could only be spotted too late. 

But with the increase of automation and AI in the near future, PV professionals will certainly need to change with the times, as many old processes that have continued for decades will now be outdated. Those who cannot adapt to a new way of thinking and understanding will quickly find themselves obsolete, whether the AI was instrumental in that or not. 

It is estimated that around 90% of AERs are currently unreported: it is vital to both patients and the drug industry as a whole that this rate is lessened. AI and ML are the most efficient and cost-effective means by which to do this. They are already yielding huge returns on investment in many companies, and as the technology grows in the near future will yield much, much more. But it is also important to ensure that automation and AI are not blindly implemented to the detriment of the task they’re working towards: the lesson that must be learnt is that even this incredible technology has limits and areas of caution. Being aware of these means pharma can increasingly help the people it set out to help, and streamline costs and processes into the bargain. 

Joshua Neil, Editor
Proventa International

The post AI and Pharmacovigilance ADR processing appeared first on Proventa International.

Across the board, negative actions both companies have undertaken in the past are being used as a reason to block the deal. The idea gathered from criticism of the merger is not so much fear of future actions as simple distrust of the companies involved: a reactionary response to the behaviours of the players involved. 

Abbvie’s initial offer on Allergan was a strong one, pushed higher after Allergan shares rose amid recent speculation of a split. The pharma giant offered 0.866 of its own shares as well as $120.30 in cash for every Allergan share, a 45% premium on Allergan’s shares that week. Allergan shares had been falling in recent months, as investors looked for a way out for the company and pressure mounted for a split that could have boosted the organisation’s ailing fortunes. 

Should the deal close, Abbvie will become the fourth largest pharmaceutical company in the world, leading in a number of areas. Abbvie CEO Richard Gonzalez, who said he had already identified $2 billion in cost cuts, is set to run the merged entity until 2023. Allergan’s current CEO and chairman, Brent Saunders, will take a board seat in the merged entity alongside another representative. 

But following criticism from the wider industry, last week a number of advocacy groups and unions wrote to the U.S. Federal Trade Commission (FTC) to suggest increased scrutiny over the purchase, citing the strength of the new entity in a market heavily criticised for rising drug prices and big pharma consolidation.

In particular, the organisations warned that a merger could see insurers or pharmacy benefit managers (PBMs) receiving more incentives such as volume-based rebates, an oft-cited reason for the steady hike in commercial drug prices, and that exclusionary conditions PBMs would have to sign could “hamper the ability of rivals to compete”.

There’s a level of undeniability about what the letter suggests – consolidation is a major worry for many within the pharma industry, and naturally a merger creating an entity so large must be scrutinised closely for anti-competitive worries. But on closer examination, doubts arise as to whether the Abbvie-Allergan deal really warrants such consternation on the part of so many, or whether another, subtler reason is behind the aggression shown towards the merger. 

It’s certainly true that while there is a scarcity of large, viable acquisition targets currently in the market, Abbvie’s move on Allergan surprised many due to the latter’s recent performance. Leading analysts have suggested that Allergan was not an “expensive name” on valuation, and in 2018 generated fewer total sales than Humira alone did for Abbvie.

Financial news and analysis site Barron’s suggested the Allergan purchase was “not the right remedy”, citing the looming end of patent protection for both Abbvie’s Humira and Allergan’s Botox as a reason for doubting the effectiveness of the merger. Since 2014, shareholders in companies that have spent more than $10 billion on a pharma acquisition have lost around 11% share value. Though Abbvie has two potential blockbusters, Skyrizi and Imbruvica, in the works, the merger will still leave the entity around $18 billion in debt by the end of 2021. 

Though just as many analysts have suggested the merger will reap a profit for Abbvie, citing diversification and particularly a move towards the aesthetics market, the conflicted messages across the sector point to one thing: this mega-merger isn’t quite the titan the companies’ critics claim it to be. While there are certainly risks to the sector to allow such a giant to emerge unscathed from a regulatory review, there is room for doubt over whether healthy competition is the only reason so many critics have emerged.

In their recent letter to the FTC, the unions and public interest groups cited the companies’ past actions as a major reason for the need to block the deal. They criticised Allergan for its recent attempts to transfer Restasis patents to the St. Regis Mohawk Native American tribe to avoid patent challenges and delay generics entering the market. Abbvie for its part was cited over past anti-competitive practices and price hikes involving Humira. 

In their letter the organisations noted that the two companies have: “engaged in a wide variety of anti-competitive conduct to stifle competition including restrictive contracting practices and intellectual property abuse.” They added that they hoped the investigation would “be part of a reinvigorated broader look at pharmaceutical drug mergers.”

Other analysts, such as Katy Milani at the Roosevelt Institute, focused on the nature of monopoly itself, as opposed to the real-world effects of this particular merger, as well as the effect of pharma consolidation generally on patients.

Given what has been made of the Abbvie-Allergan deal by experts, claims of a pharma titan that would quash competition seem ill-founded: with at least two of their mega-blockbusters open to generic equivalency by 2023, the deal will create a more diverse, disparate company than either of its entities were beforehand. Differences in culture and a lack of two market-cornering drugs could well neuter a potentially fearsome beast. 

But the fact that the market has reacted so strongly to the merger still indicates a hugely important lesson for both companies, and every other pharma giant: the sins of the past have greater implications than merely a temporary drop in stock prices. With the compounding of events seen by the public in a negative light, big actions such as the Abbvie-Allergan merger will become increasingly hard as past actions are thrown up as blockades and regulators face ever-increasing pressure to scrutinise the minutiae of deals. The Abbvie-Allergan deal may well go ahead, but without the criticisms being levelled at their past actions, they would have found the merger a great deal easier. 

Joshua Neil, Editor
Proventa International

The post The Abbvie-Allergan Deal: A Matter of Distrust? appeared first on Proventa International.