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Abstract The urgent need for safe, efficacious, and accessible drug treatments to treat coronavirus disease 2019 (COVID‐19) prompted a global effort to evaluate drug repurposing opportunities. Pyronaridine and amodiaquine are both components of approved antimalarials with in vitro activity against severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). In vitro activity does not always translate to clinical efficacy across a therapeutic dose range. This study applied available, verified, physiologically based pharmacokinetic (PBPK) models for pyronaridine, amodiaquine, and its active metabolite N‐desethylamodiaquine (DEAQ) to predict drug concentrations in lung tissue relative to plasma or blood in the default healthy virtual population. Lung exposures were compared to published data across the reported range of in vitro EC50 values against SARS‐CoV‐2. In the multicompartment permeability‐limited PBPK model, the predicted total Cmax in lung mass for pyronaridine was 34.2 μM on Day 3, 30.5‐fold greater than in blood (1.12 μM) and for amodiaquine was 0.530 μM, 8.83‐fold greater than in plasma (0.060 μM). In the perfusion‐limited PBPK model, the DEAQ predicted total Cmax on Day 3 in lung mass (30.2 μM) was 21.4‐fold greater than for plasma (1.41 μM). Based on the available in vitro data, predicted drug concentrations in lung tissue for pyronaridine and DEAQ, but not amodiaquine, appeared sufficient to inhibit SARS‐CoV‐2 replication. Simulations indicated standard dosing regimens of pyronaridine‐artesunate and artesunate‐amodiaquine have potential to treat COVID‐19. These findings informed repurposing strategies to select the most relevant compounds for clinical investigation in COVID‐19. Clinical data for model verification may become available from ongoing clinical studies.

Abstract As part of a collaboration between Medicines for Malaria Venture (MMV), Certara UK and Monash University, physiologically‐based pharmacokinetic (PBPK) models were developed for 20 antimalarials, using data obtained from standardized in vitro assays and clinical studies within the literature. The models have been applied within antimalarial drug development at MMV for more than 5 years. During this time, a strategy for their impactful use has evolved. All models are described in the supplementary material and are available to researchers. Case studies are also presented, demonstrating real‐world development and clinical applications, including the assessment of the drug–drug interaction liability between combination partners or with co‐administered drugs. This work emphasizes the benefit of PBPK modeling for antimalarial drug development and decision making, and presents a strategy to integrate it into the research and development process. It also provides a repository of shared information to benefit the global health research community.

Abstract Chemoprophylactics are a vital tool in the fight against malaria. They can be used to protect populations at risk, such as children younger than the age of 5 in areas of seasonal malaria transmission or pregnant women. Currently approved chemoprophylactics all present challenges. There are either concerns about unacceptable adverse effects such as neuropsychiatric sequalae (mefloquine), risks of hemolysis in patients with G6PD deficiency (8‐aminoquinolines such as tafenoquine), or cost and daily dosing (atovaquone–proguanil). Therefore, there is a need to develop new chemoprophylactic agents to provide more affordable therapies with better compliance through improving properties such as pharmacokinetics to allow weekly, preferably monthly, dosing. Here we present a pharmacokinetic–pharmacodynamic (PKPD) model constructed using DSM265 (a dihydroorotate dehydrogenase inhibitor with activity against the liver schizonts of malaria, therefore, a prophylaxis candidate). The PKPD model mimics the parasite lifecycle by describing parasite dynamics and drug activity during the liver and blood stages. A major challenge is the estimation of model parameters, as only blood‐stage parasites can be observed once they have reached a threshold. By combining qualitative and quantitative knowledge about the parasite from various sources, it has been shown that it is possible to infer information about liver‐stage growth and its initial infection level. Furthermore, by integrating clinical data, the killing effect of the drug on liver‐ and blood‐stage parasites can be included in the PKPD model, and a clinical outcome can be predicted. Despite multiple challenges, the presented model has the potential to help translation from preclinical to late development for new chemoprophylactic candidates.

Volunteer infection studies using the induced blood stage malaria (IBSM) model have been shown to facilitate antimalarial drug development. Such studies have traditionally been undertaken in single‐dose cohorts, as many as necessary to obtain the dose‐response relationship. To enhance ethical and logistic aspects of such studies, and to reduce the number of cohorts needed to establish the dose‐response relationship, we undertook a retrospective in silico analysis of previously accrued data to improve study design. A pharmacokinetic (PK)/pharmacodynamic (PD) model was developed from initial fictive‐cohort data for OZ439 (mixing the data of the three single‐dose cohorts as: n = 2 on 100 mg, 2 on 200 mg, and 4 on 500 mg). A three‐compartment model described OZ439 PKs. Net growth of parasites was modeled using a Gompertz function and drug‐induced parasite death using a Hill function. Parameter estimates for the PK and PD models were comparable for the multidose single‐cohort vs. the pooled analysis of all cohorts. Simulations based on the multidose single‐cohort design described the complete data from the original IBSM study. The novel design allows for the ascertainment of the PK/PD relationship early in the study, providing a basis for rational dose selection for subsequent cohorts and studies.