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Optimize Your COVID-19 Candidate Therapies with MIDD

By Eileen Doyle and Suzanne Minton

The world is in the grip of a deadly pandemic. Coronavirus Disease 2019 (COVID-19) has caused over 15 million confirmed infections globally and more than 600,000 deaths. With no approved drugs or vaccines, developing COVID-19 therapies is a global health priority.

A Clinical and Translational Science commentary shared insights on optimizing COVID‐19 candidate therapeutics by drawing on previous anti-infective drug development experiences including H5N1 and pH1N1 influenza outbreaks.1 Clinical pharmacology, real world evidence (RWE), and model-informed drug development (MIDD) were among the recommended approaches to increase the probability of program success. In this blog, we will go into more depth on how MIDD can inform and accelerate a COVID-19 therapeutics program.

COVID-19: What is it, and whom is it affecting?

Human coronaviruses were first identified in the mid-1960s.2 They are a group of diverse, enveloped, positive-sense single-stranded RNA viruses that cause respiratory, enteric, hepatic, and neurologic diseases of varied severity in a range of animals, including humans. In fact, approximately 20% of common colds are caused by coronaviruses.  Most people get infected with one or more of these viruses during their lifetime.3  

COVID-19 is caused by one of the three known human coronaviruses that lead to severe acute respiratory issues:  Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Because this virus is new in humans, the research community has gained all their experience with it over the past 8 months or so.  Thus, most current knowledge of how to battle SARS-CoV-2 is informed by the etiology and epidemiology of two other coronaviruses known to cause serious human disease: severe acute respiratory syndrome (SARS-CoV; SARS), which caused an outbreak in 2003 and Middle East respiratory syndrome CoV (MERS-CoV; MERS), which caused an outbreak in 2012.

Symptoms of COVID-19 are similar to those of SARS and MERS. According to a WHO analysis, the most common symptoms (with % of people experiencing them) of COVID-19 are fever (88%), dry cough (68%), fatigue (38%) and shortness of breath (19%). While most patients infected with COVID-19 experience mild or moderate symptoms, older people and those with co-morbidities are believed to be at higher risk for developing severe and potentially fatal respiratory disease. Healthcare workers who are in constant contact with COVID-19-positive patients also face higher risks. Though the burden of COVID-19 and epidemiologic risk factors in children have not yet been well defined, most cases appear to occur in people over the age of 18 years.

What interventions are being employed to fight the COVID-19 pandemic?

The exponential nature of the infection, combined with our ability to move globally as a society, pits time against us. In the absence of a vaccine or approved medications, hygiene, social distancing and quarantine of COVID-19 patients are the primary interventions being employed. Thus, there is an urgent need to develop safe and effective drugs to treat COVID-19 patients, to use as pre-exposure prophylaxis (PrEP) for healthcare workers, and to use as post-exposure prophylaxis (PEP) to prevent COVID-19 infection in people exposed to SARS-CoV-2. The drugs under investigation include both novel and repurposed drugs (small molecule drugs and biologics).

Challenges in developing drugs for COVID-19

Developing drugs for COVID-19 presents numerous challenges. Because the virus was just isolated, basic research needs to be conducted. Time and materials for that research compete with the need to support diagnostic teams and clinics. Virologists and others investigating infectious diseases at universities, research centers and hospitals have shifted their focus to studying SARS-CoV-2 to determine how this virus creates cellular environments to support its replication and to address why some people develop more serious disease than others. Just as genetics and biomarkers are important in drug development as a whole, there is a possibility they will play a key role in COVID-19 treatment.

The dose must be optimized to maximize efficacy and minimize toxicity. To do this, we need to understand the impact of intrinsic (liver/kidney function, age, pregnancy status) and extrinsic factors (e.g. diet and concomitant medications) on pharmacokinetic (PK) variability. We also need to consider the disease including the site of infection, the time-course of viral shedding, and the onset of “cytokine storm” and downstream sequelae). Ideally, trials will be adaptive and designed to be optimized along the way so that they have the greatest likelihood of success.

While randomized controlled trials (RCTs) are and will continue to be a mainstay of drug development, they are very expensive and time consuming. RCTs also cannot investigate all scenarios. For example, sensitive populations such as children and pregnant women are difficult to recruit into RCTs for practical and ethical reasons. Within a pandemic, many other factors are also at play including logistical challenges to patient recruitment and clinical trials operations, rapidly evolving information that may result in loss of equipoise for placebo comparisons, and in general, variable quality or “gray” data. To expedite developing drugs to fight this pandemic, alternative approaches are needed.

How can MIDD support antiviral drug development?

MIDD (also known as modeling and simulation; M&S) integrates two transformative technologies: computer-aided mathematical simulation and biological sciences. It uses pre-clinical and clinical data along with published industry data to elucidate the relationships between drug exposure, drug response, and patient outcomes. Models quantify a host of crucial development decisions related to the right pathway, target, molecule, dose, and commercial performance. MIDD encompasses a range of approaches including both empirical methods— population pharmacokinetic/pharmacodynamic (pop PK/PD) M&S, model-based meta-analyses (MBMA), and clinical trial simulation— and mechanistic methods such as physiologically-based pharmacokinetic (PBPK) M&S. We will explain how each of these approaches can inform COVID-19 drug development.

PopPK

Each individual subject has a set of PK/PD parameters based on their individual characteristics, drug concentration, and drug effect information. Thus, in individual model fitting, a full PK/PD profile is required to generate the PK/PD parameters of interest. Pop PK/PD M&S expands upon individual PK/PD analysis by (1) relating individual PK/PD parameters to a set of theoretical “typical” PK/PD parameters and (2) quantifying the impact of known information (e.g., age, sex, weight, phenotype, etc.) on the variability in the individual PK/PD parameters.

As COVID-19 patients with severe disease may include subpopulations with organ impairment, it’s important to consider the potential impact of renal and hepatic impairment on dosing. Many phase 2/3 clinical trials exclude patients with renal or hepatic impairment. If the exclusion criteria are adjusted to include patients with organ impairment in efficacy trials, then PopPK models can be used to support the renal and hepatic impairment sections of the drug label. This then allows sponsor to avoid the time and expense of performing dedicated renal/hepatic impairment studies.

Likewise, pop PK/PD modeling can be used to understand how interpatient variability affects the effect of drugs. For instance, immunocompromised patients may need longer durations of treatment with anti-COVID-19 drugs to clear their infections compared to patients who have normally functioning immune systems.

PBPK M&S

PBPK modeling and simulation is a mechanistic approach that links in vitro data to in vivo absorption, distribution, metabolism, and excretion (ADME) and PK/PD outcomes to explore potential clinical complexities prior to human studies and support decision-making in drug development.

One of the best-known applications for PBPK M&S is in predicting DDIs. Antiviral drugs are often administered as combination therapies, and patients are often taking other medications concomitantly. Thus, DDI prediction is essential to ensuring the safety of any drug developed for COVID-19. PBPK models can predict the DDI potential of an investigational drug as an enzyme substrate or an enzyme perpetrator. Alternatively, the sponsor can use a PBPK model to inform the need for conducting additional studies.

For a drug to treat COVID-19 infection, it must reach its site of action in the lung. Mechanistic approaches like PBPK are being used to understand drug disposition at the site of action.

Clinical Trial Simulation

The astronomical cost of conducting clinical drug trials means that a suboptimal trial design adds significant risk to a drug program. Clinical trial simulation (CTS) allows drug developers to test different trial designs in silico before exposing patients to an experimental antiviral drug.

Clinical trial simulation can allow researchers simulate virtual trials with varying scenarios— different numbers of subjects, inclusion/exclusion criteria, predicted drug effect magnitude— to determine which designs provide reasonable likelihood of success.

An application for CTS is in optimizing dosing for sensitive populations, including pediatrics. While children with COVID-19 generally develop mild disease, some children have succumbed to this disease highlighting the importance of considering this population in developing therapies.

Children are a heterogeneous population, especially neonates and infants, as organ maturation affects drug exposure and response. In silico approaches like CTS can help us to assess which dosing regimens are most likely to achieve target attainment while minimizing the risk to pediatric patients. For example, clinical trial simulation was used to evaluate the probability of target attainment for the antibiotic meropenem in various age groups and degrees of fluid overload in children receiving continuous renal replacement therapy (CRRT).4

MBMA

A hurdle in developing therapies for a new, emerging pathogen is that the data collected are often crude and disparate. MBMA is an approach for mining insights on interventions (and their dosing) from across pooled trials, which by themselves may not have adequate information to address specific hypotheses. This approach involves a systematic search and tabulation of summary results from public sources, which may be combined with proprietary clinical trial data. These data are then analyzed to characterize the impacts of drug class, drug, dose, and time on the response(s) of interest. The influence of study population characteristics or the trial conduct may also be explored.

MIDD: A critical tool in fighting the COVID-19 pandemic

Successful drug development and commercialization requires getting critical decisions right:

  • What is the exposure-response relationship for the drug?
  • What is the optimal dosing strategy?
  • Which patients would derive greatest benefit from it?

MIDD uses mathematical and statistical models to quantify drug, disease, and trial information to help address these decisions. Hopefully, you now have a better understanding of how MIDD can help drug developers to make the most of available information and accelerate developing critical therapies for COVID-19.

To learn more about how translational and clinical pharmacology and quantitative science are being leveraged to support development strategies, clinical trial designs, and dosing optimization for COVID-19 therapeutics, please watch this webinar.

  1. Rayner CR, Smith PF, Hershberger K, Wesche D. Optimizing COVID-19 candidate therapeutics: Thinking without borders. Clin Transl Sci. 2020. doi: 10.1111/cts.12790 [doi].
  2. https://www.cdc.gov/coronavirus/types.html
  3. Killerby ME, Biggs HM, Haynes A, Dahl RM, et al. Human coronavirus circulation in the United States 2014 – 2017external icon.  Journal of Clinical Virology. Vol 101; 2018 Apr; 101:52-6
  4. Nehus EJ, Mouksassi S, Vinks AA, Goldstein S. Meropenem in children receiving continuous renal replacement therapy: Clinical trial simulations using realistic covariates. J Clin Pharmacol. 2014;54(12):1421-1428. doi: 10.1002/jcph.360 [doi]