By Dr Craig R. Rayner, FRCP Edin, PharmD, MBA, President, Integrated Drug Development at Certara
Right at the start of the Covid-19 pandemic, medical researchers began questioning whether there were any existing, on-market drugs that might prove effective against SARS-CoV-2. They wanted to offer healthcare professionals and patients some known therapeutic options while researchers quickly began developing more targeted drugs and prophylactic vaccines. They turned to model-informed drug development (MIDD) approaches to provide a rational, quantitative framework for assessing the viability of different repurposed drugs as potential Covid-19 therapies.
MIDD incorporates in-vitro, pre-clinical and clinical pharmacokinetic and pharmacodynamic data into computer models to improve decision-making in clinical research.
This approach is used to determine the most appropriate drug doses, especially for vulnerable populations such as elderly or pediatric patients and pregnant women; identify the likelihood of drug-drug interactions; and design the most efficient clinical trials. As the process is iterative, the models are refined as additional data are collected, driving higher quality decision making.
Covid-19 is a complex disease to manage, because anti-viral drugs are required to combat its initial impact on the body, then anti-inflammatory drugs to counter its effect on the lungs, and later, for severe cases, immunomodulatory drugs are needed to ameliorate the hyperactive immune response or ‘cytokine storm’ that it generates.
Both mechanistic and empirical MIDD are being employed against Covid-19. Researchers have been taking in vitro and animal modeled data about the SARS-CoV-2 virus and using that knowledge to predict whether an existing drug, which showed activity against the virus in vitro, might have potential as a Covid-19 therapeutic.
For example, physiologically based pharmacokinetic (PBPK) modeling was used to show that even high doses of the anti-parasitic therapy ivermectin, which inhibited SARS-CoV-2 in the laboratory, would not be present in high enough concentrations in virus-infected lung tissue to potentially be effective in Covid-19 disease. This translational science has been useful to adjudicate recommendations regarding ivermectin use in the face of mixed clinical outcomes from studies of variable quality.1
Different model-based approaches are being explored to inform thinking about repurposed medications. Modeling is being used to answer questions such as: Where in the disease path should the medication be applied? How should it be used? Where would it have the greatest impact?
This approach replaces hope with translational evidence, enabling clinical studies to focus on candidates and combinations that have the greatest likelihood of success.
Covid-19 has created a scientific environment that is moving at a furious pace. The science changes hourly. When a clinical trial protocol is ready, oftentimes the science has evolved such that it is no longer relevant, the questions have been answered, or a different approach appears more promising. It requires researchers to be extremely efficient amid information flowing at hypervelocity.
In the early stages of the Covid-19 pandemic, global efforts were somewhat uncoordinated, which led to significant inefficiencies. At one point, there were more than 1,600 clinical trials underway globally, competing for resources – funding, equipment, supplies, staff, and patients. Some studies were duplicates, others were underpowered, reducing the scientific knowledge that could be gained from them. In some cases, it proved impossible to determine conclusively the efficacy of the study drug.
Politics also complicated the situation because if a high-ranking official stated that a drug worked, it changed patient behavior and could effectively remove the control arm from a study. It fundamentally changed the way in which clinical trials could be conducted.
Another challenge was trying to overcome dogma in approaches. An optimal pandemic response demands nimble methods and intelligent data integration. A large-scale, randomised controlled trial is not the only way to test a hypothesis and answer a question. There needs to be greater consideration of the clinical and translational pharmacology involved and more emphasis placed on dose and dose finding.
That is particularly important in the context of manufacturing, especially with the current global demand for Covid-19 vaccine production and administration. If it were determined that a person needed only one half of the vaccine dose, there would be enough vaccine available to inoculate twice as many people, and the vaccine could effectively be rolled out twice as fast. There is very tight interplay between dose regimen and clinical pharmacology science, and manufacturing and logistics, a relationship that has been mostly ignored. This is one of the biggest lessons learned from Covid-19.
If the dose regimen is not thought through properly in advance, a massive opportunity is missed. Once a randomised controlled trial has been completed, it is extremely difficult to go back to see if perhaps a lower dose would have been equally effective. It is hard to work backwards without the evidence base to support it.
Pooling clinical data
Certara responded to the early morass of global Covid-19 studies by establishing the Covid Clinical Outcomes Database and creating a robust, rational framework within which the safety and efficacy of repurposed drugs could be assessed.
The Certara team reviews all the global Covid-19 clinical trial and observational study results as they are published, harvesting that emerging data and conducting data analysis and model-based meta-analyses (MBMA). This approach allows real-world evidence from thousands of clinical trials and observational studies (both Covid-related and pertaining to the drug’s original indication) to inform current thinking.
These modeling techniques help manage heterogeneity among trials – some studies use a high dose of a drug while others use a low dose or a different drug combination – and enable the data to be mined fully.
Developing a drug repurposing workbench
Certara is also curating all the available preclinical data and has collaborated with the Bill & Melinda Gates Foundation (BMGF) to develop a drug repurposing workbench (www.covidpharmacology.com) that enables researchers to make faster decisions about potential drug repurposing options. The Covid-19 Pharmacology Resource Center facilitates global collaboration by providing sophisticated, complimentary tools that researchers can use to identify candidates for Covid-19. It includes an in silico workbench with a compound screening dashboard, Covid epidemiological and viral kinetic models, pharmacokinetic/pharmacodynamic simulators to compare lung concentrations of therapeutic candidates to SARS-CoV-2 in vitro susceptibility, a global clinical trial tracker, and access to webinars and commentaries by world-renowned experts. It is a complete decision and knowledge management platform, which brings together preclinical screening information and emerging real-world evidence into a single interface. Researchers can use it to answer questions about whether the in vitro information was predictive or associated with emerging real-world evidence. The platform helps them to visualise the data and make decisions.
Identifying promising monotherapies
As on-market drugs are being tested to identify those that can be effectively repurposed to treat Covid-19, it is becoming apparent that many monotherapies, whether traditionally used to treat HIV, rheumatoid arthritis, or lupus, do not work well against Covid-19 in the clinic. It transpires that one needs to be quite lucky to get a drug that was developed for another disease and find it effective for Covid-19.
But there have certainly been some drugs which have borne fruit for repurposing. For example, the rheumatoid arthritis drug tocilizumab exhibited potential as a Covid-19 therapy in our modeling and has now shown promise in treating later-stage disease. Dexamethasone, which is used to reduce inflammation, has also proven effective.
Transitioning to combination therapies
While repurposed monotherapies have not proven as effective as many people had hoped for treating Covid-19, they have spurred the emergence of a new paradigm for drug combination development for pandemics.
This new approach, which uses an in-silico model to conduct combination drug screens, was published by Certara scientists recently in Clinical Pharmacology & Therapeutics.2 It uses a quantitative systems pharmacology (QSP) viral cell cycle model to identify drugs with different modes and areas of action that could be rationally combined.
The combinations are then tested in a preclinical environment to make sure that there are no unintended consequences when they are used together. Occasionally, when drugs are combined, antagonism can occur, and this in vitro step helps to check that the compounds will not unintentionally neutralise each other’s activity.
If there is no evidence of antagonism, then multiple drug combinations can be tested in tandem. The new model proposes testing groups of three drugs together.
Among the compounds being considered are colchicine, pegylated interferon, budesonide, sofosbuvir, daclatasvir, camostat, nafamostat, and intranasal heparin. There are also several other compounds that are interesting from a mechanistic perspective, which might have real-world evidence to support them.
To illustrate this approach, consider a study with four arms in which drugs one, two, and three are compared with drugs four, five and six, and drugs seven, eight and nine, and a placebo. The first cohort is run, and the results are examined. In this scenario, drug combination one, two and three did not produce any better results than the placebo, so that arm is stopped. Then, the study continues with combinations four, five and six, and seven, eight and nine. Combination four, five and six initially shows promise, but it proves not to be significant over time, so that arm is also dropped. At that point, only drug combination seven, eight and nine remains.
As combination seven, eight and nine was developed rationally, and it appears to be working, the placebo arm is stopped. At that stage, drugs seven, eight and nine are studied individually to determine their relative contributions to the positive effect. Are they all active contributors, or is one a passenger? The goal is to determine the simplest efficacious regimen.
Taking the traditional approach and investigating all the drugs individually would take years. That study would begin with ten arms (nine drugs and the placebo arm). Once that cohort was complete, additional drugs would be added incrementally. While the traditional approach would require ten arms in the first cohort, the new model needs only four. As a result, the new model is about one third of the cost and takes about one third of the time – vitally important differences in a pandemic situation.
The actual size of the trial depends on the endpoints being investigated. For example, studying an early viral shedding endpoint might require only 50-100 participants per arm. Therefore, a trial following this new approach might take 200 to 400 patients. Whereas taking the traditional route would require thousands of patients to test all the possible drug combinations.
A lot of the knowledge gained, and many of the tools and approaches developed to explore repurposing drugs are now also being applied to investigate new compounds that have been specifically developed for Covid-19. For example, Certara created a QSP model to understand the common challenge of immunogenicity against antibody drugs, which is now being turned on its head to examine the potential dosing of new vaccines.
Certara has supported many of the compounds currently under investigation for Covid-19 with some level of modeling or development strategy input. It is also actively collaborating with BMGF on drug repurposing considerations through the Covid Therapeutics Accelerator.
An interdisciplinary approach is also being pursued to address some of the complex health and economic issues that Covid-19 presents. Consider, for example, if some of the repurposed compounds, new compounds, such as monoclonal antibody cocktails, or vaccines reduced SARS-CoV-2 viral shedding, but did not make the patient feel or function better, how would you view their utility? Would you advocate using them to reduce quarantine periods or get people ready to travel in airplanes?3
The Covid-19 pandemic has underscored the importance of preparedness. Researchers around the world need to be thinking about disease x, the next Covid, now and not waiting to try new strategies until the battle is underway. We need to work together to anticipate what it will be, identify the right drugs to treat it, and the most appropriate doses.
Many aspects of a virus’ evolution are predictable, such as the emergence of new variants, some of which are more contagious. After a virus starts a pandemic, there is usually some movement in its fitness because it is geared to want to spread. Transmissibility generally increases in these circumstances, but fortunately often at the expense of virulence.
Covid-19 vaccine models can provide guidance on how best to manage emerging virus variants. They allow researchers to simulate “what-if” scenarios when it is not possible or practical to run additional clinical trials.
For example, a model describing how a Covid-19 vaccine triggers an immunogenic response can be used to answer questions such as, “If someone receives two doses of the vaccine, how long will their protection last?” Can someone wait an extra two weeks between vaccine doses without negatively impacting their effectiveness? Is it okay to mix these two vaccines if there is a supply shortage? This type of intelligence can help suppliers to manage the necessary logistics.
Embracing global collaboration
A pandemic is global, so it requires a global view and global engagement to effectively manage it. Ultimately, any program is only as effective as its weakest part.
To be successful, future pandemic preparedness requires better planning. Certara is advocating for the development of a network of pre-clinical and clinical competency-based centres in different geographies around the world. All their work would be connected and coordinated, so there is no duplication of effort.
To appreciate the potential benefits of this concept, consider the clinical research done on hydroxychloroquine during the Covid-19 pandemic. An MBMA was conducted of hydroxychloroquine’s putative effectiveness, which included both randomised control trials and cohort studies. In just one of those meta-analyses, there were 27,342 patients recruited across about 14 studies, and that was only a subset of the studies. It took the involvement of more than 27,000 patients to show there was no positive outcome. It was an exceedingly inefficient approach.
Furthermore, if that effort was priced based on typical clinical trial costs, depending on what analog was used, it cost between $1.5 billion and nearly $5 billion to get that question answered. With a more coordinated effort, that question could have been answered effectively with one specific trial.
Large platform trials, such as the Recovery, Solidarity and Together trials for Covid-19, which focused on different parts of the disease spectrum, and shared information, were extremely important.
But if researchers are looking at the same drug doses in all the studies, that represents a large, missed opportunity and a lot of time wasted. Researchers need to also be thinking about dose much earlier.
If the translational pharmacology is centrally coordinated, and all the available data and evidence are combined, modeling and simulation can be used to determine not only dose, but also what drug combinations to select and how to employ them in clinical studies.
The goal is to optimise all the clinical trials, making every person’s participation more valuable because they are helping to answer a new question and not recreate a study that has already been conducted.
Amid the Covid-19 pandemic, modeling and simulation provided a rational approach with which to quickly identify the existing, on-market drugs that were most likely to be effective against SARS-CoV-2 in the clinic. Equally, it efficiently determined which drugs could not be repurposed for that function, thereby saving resources, and redirecting efforts toward more promising compounds. That early work with monotherapies led to the development of new modeling approaches and adaptive clinical trials approaches for combinations, which allow multiple drug combinations to be evaluated together rapidly and effectively for activity against Covid-19.
Lessons learned from, and modeling advances spurred by, Covid-19 will help improve the global response to the next emerging infectious disease. With the right tools, preparation, and cooperation, global medical researchers may be able to prevent the next outbreak from becoming an epidemic.
Figure 1: Model‐informed combination repurposing strategy for Covid‐19
Reprinted with permission from Clinical Pharmacology & Therapeutics2
About the author
Dr. Craig Rayner is President, Integrated Drug Development at Certara in Princeton, New Jersey, USA. Certara uses proprietary biosimulation software and technology to transform traditional drug discovery and development. Dr. Rayner is also an Adjunct Associate Professor in Pharmaceutical Science and a Distinguished Alumnus at Monash University in Melbourne, Australia. Earlier this year, Dr. Rayner was inducted as a fellow of the Royal College of Physicians of Edinburgh in Scotland.
- Why You Should Not Use Ivermectin to Treat or Prevent COVID-19. March 5 2021. https://www.fda.gov/consumers/consumer-updates/why-you-should-not-use-ivermectin-treat-or-prevent-covid-19?c
- Craig R. Rayner, Patrick F. Smith, David Andes, Kayla Andrews, Hartmut Derendorf, Lena E. Friberg, Debra Hanna, Alex Lepak, Edward Mills, Thomas M. Polasek, Jason A. Roberts, Virna Schuck, Mark J. Shelton, David Wesche, Karen Rowland‐Yeo. Model‐Informed Drug Development for Anti‐Infectives: State of the Art and Future. Clinical Pharmacology & Therapeutics. 8 February 2021. https://doi.org/10.1002/cpt.2198.
- Hazem Hassan, Nidal Huniti, Roman Casciano, Mohamed Kamal, Andreas Kuznik, Patrick Smith. Innovative Pharmacology to Health Economics Approach Using A Multi-Scale COVID-19 Transmission Model. ASCPT 2021 Conference Presentation. https://www.certara.com/conference/ascpt-2021.
- Michael Dodds, Yuan Xiong, Samer Mouksassi, Carl Kirkpatrick, Katrina Hui, Eileen Doyle, Kashyap Patel, Eugène Cox, David Wesche, Fran Brown, Craig R. Rayner. Model‐Informed Drug Repurposing: a Pharmacometric Approach to Novel Pathogen Preparedness, Response and Retrospection. British Journal of Clinical Pharmacology. 3 February 2021. https://doi.org/10.1111/bcp.14760