Model-Informed Drug Development Addresses COVID-19 Challenges
Drug developers know that the odds of anyone compound demonstrating safety and efficacy for a disease and its affected populations are low. How can drug developers improve these odds and increase the efficiency and effectiveness of drug development? One useful tool is model-informed drug development (MIDD), which uses computer models to inform the design of clinical trials or to run simulations when human or animal trials are not feasible. By ensuring that appropriate drugs are advanced and the clinical trial design is optimized, MIDD helps drug companies develop therapies for emerging diseases like COVID-19.
MIDD, also called modeling and simulation (M&S), is the application of in silico quantitative models in drug development to facilitate decision-making. MIDD is centered on knowledge and inferences generated from integrated mathematical models of the physicochemical characteristics of a molecule, its disposition in the body, and its mechanism of action, and how the drug might affect a disease from both an efficacy and a safety perspective. MIDD informs, reduces, and sometimes can eliminate the need for clinical trials, guiding decision-making on dose regimen optimization, safety, and toxicology, as well as clinical trial design and supportive evidence for efficacy.
Regulatory Framework
Encouraged by global regulators, MIDD is used in virtually all novel drug development programs. Specifically, the US FDA has MIDD provisions under the Prescription Drug User Fee Act (PDUFA) VI.
Work is ongoing to create international guidelines to standardize the validation and verification of MIDD models for use in regulatory filings. Two concerns in guideline development are (a) the reliability of models is only as good as the data used to generate them,
Applications
MIDD has many applications (Figure 2). It is used to determine product development go/no-go decisions, moving the most promising candidates forward and stopping development of prospects that are unlikely to be effective. MIDD is also employed to translate data from in vitro to in vivo research, animals to humans, smaller to larger/more diverse populations, adults to pediatric patients, and so on. Additionally, it can be used to model drug performance in different organs, evaluate drug-drug interactions (DDIs), and analyze optimal drug combinations.
Overall, MIDD helps drug developers answer key questions (Table 1). By leveraging expertise in clinical pharmacology, accelerated regulatory path-ways, and M&S, companies focusing on drug development can partner those with specific expertise in MIDD protocol development, thereby expediting trial enrollment.
MIDD is being used to aid in triaging COVID-19 drug candidates according to their likelihood of demonstrating safety and efficacy.
PBPK and POPPK Modeling
MIDD encompasses a range of techniques to inform drug development. Physiologically based pharmacokinetic (PBPK) and population pharmacokinetic (PopPK) modeling are two broadly used MIDD approaches. The former is a mechanistic, “bottom up” technique, and the latter is an empirical, “top down” technique.
PBPK M&S links in vitro data to in vivo absorption, distribution, metabolism, and excretion, as well as pharmacokinetic/pharmacodynamic (PK/PD) outcomes, to explore potential clinical complexities prior to human studies and support decision-making in drug development. 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.
Population PK/PD M&S expands upon individual PK/PD analysis by (a) relating individual PK/PD parameters to a set of theoretical “typical” PK/PD parameters, and (b) quantifying the impact of known information (e.g., age, sex, weight, phenotype) on the variability in the individual PK/PD parameters.
Topic | Questions |
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Selection of optimal drug candidates |
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Trial optimization |
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Safety and toxicity |
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Dose selection, special populations, and formulation |
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Regulatory and commercial success |
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Development of COVID-19 Therapeutics
Several lines of investigation are open for COVID-19 drug development, and multifaceted modeling approaches are required to handle the different scenarios. MIDD is being used to aid in triaging COVID-19 drug candidates according to their likelihood of demonstrating safety and efficacy. Simulations are being performed to fill critical data gaps and determine in silico the impact of both new and existing therapies.
Determining Therapeutic Dosages
It is critical to optimize the dose for any therapy intended to treat COVID-19. If the dose is too low, it may be falsely concluded that the drug is ineffective. On the other hand, if a dose is too high, the drug may cause toxicity. Recent M&S work has predicted that certain drugs under consideration for COVID-19 treatment, ivermectin and lopinavir/ritonavir (LPV/r), are not efficacious at the doses studied.
Ivermectin
Ivermectin, an antiparasitic drug, is also approved for head lice and scabies. Researchers in Australia recently announced that a single dose of ivermectin could kill SARS-CoV2 in vitro within 24 hours.
If the developers of ivermectin create a potentially effective way of administering a higher dose of the drug, such as a nasal spray or inhaled drug, PBPK modeling could be applied to determine whether ivermectin in this form could effectively reduce viral counts. However, if the higher dose of ivermectin is limited because of safety concerns, it is unlikely that this candidate will move forward.
LPV/r
The limited viral kinetic data available for COVID-19 suggest that the duration of viral shedding (i.e., the process of the virus being released into the environment after it has replicated inside the body) is longer than that for influenza, and longer treatment may be necessary to continue to fight the virus. Investigators are interested in LPV/r, an approved HIV treatment, as a potential COVID-19 therapy because it decreases HIV levels in blood and theoretically may have similar effects on SARS-COV-2.
In vitro data on the effects of LPV/r against coronaviruses indicate that the drug combination may be significantly less active against SARS-COV-2 compared to HIV-1.
A collaborative research team used PopPK models and tested a range of potential PK/PD relationships to simulate LPV/r plasma and lung exposures in adults.
Additional modeling with a loading dose could be performed to determine whether reaching higher plasma LPV/r concentrations in a shorter time can alter the COVID-19 disease course (e.g., whether the patient requires hospitalization and a ventilator).
Viral Kinetic Modeling
Another approach for advancing treatments for COVID-19 involves viral kinetic models that use mathematical equations to describe how viral load changes with time in an infected patient.
Research teams are combining viral kinetic modeling with PK/PD modeling to quantify drug effects based on their mechanism of action and evaluate in silico the efficacy of specific drug combinations in treating COVID-19 and other viral diseases.
Lung Exposure Modeling
Another MIDD approach relevant to COVID-19 involves modeling the lung exposure of drugs. To combat the tuberculosis (TB) epidemic, a research team in collaboration with several leading global health institutions developed a multicompartment permeability-limited lung model. This newly developed PBPK model helps drug developers leverage in vitro and in silico data to understand drug disposition and penetration in plasma, lung tissue, epithelial lining fluid, and TB granulomas. In addition, these tools allow researchers to simulate a range of variables—drug dose, disease state, and concomitant medications—and thus support designing more-effective drug regimens.
Conclusion
The global community of scientists is working 24/7 to identify, study, and test potential treatments and cures for many diseases, including COVID-19. In the current pandemic, there is a compelling need to speed progress, evaluate opportunities to shift regulatory paradigms, and efficiently make go/no-go decisions. It is time for researchers to open the MIDD toolbox and leverage it. Together, using multiple advanced modeling tools, collaboration, and expertise, the scientific community will prevail in finding appropriate treatments and preventive measures for COVID-19.
Acknowledgment
The authors thank Mia DeFino, MS, ELS, for medical writing support.