Model-Informed Drug Development for Anti-Infectives: State of the Art and Future.

Model-informed drug development (MIDD) has a long and rich history in infectious diseases. This review describes foundational principles of translational anti-infective pharmacology, including choice of appropriate measures of exposure and pharmacodynamic (PD) measures, patient subpopulations, and drug-drug interactions. Examples are presented for state-of-the-art, empiric, mechanistic, interdisciplinary, and real-world evidence MIDD applications in the development of antibacterials (review of minimum inhibitory concentration-based models, mechanism-based pharmacokinetic/PD (PK/PD) models, PK/PD models of resistance, and immune response), antifungals, antivirals, drugs for the treatment of global health infectious diseases, and medical countermeasures. The degree of adoption of MIDD practices across the infectious diseases field is also summarized. The future application of MIDD in infectious diseases will progress along two planes; "depth" and "breadth" of MIDD methods. "MIDD depth" refers to deeper incorporation of the specific pathogen biology and intrinsic and acquired-resistance mechanisms; host factors, such as immunologic response and infection site, to enable deeper interrogation of pharmacological impact on pathogen clearance; clinical outcome and emergence of resistance from a pathogen; and patient and population perspective. In particular, improved early assessment of the emergence of resistance potential will become a greater focus in MIDD, as this is poorly mitigated by current development approaches. "MIDD breadth" refers to greater adoption of model-centered approaches to anti-infective development. Specifically, this means how various MIDD approaches and translational tools can be integrated or connected in a systematic way that supports decision making by key stakeholders (sponsors, regulators, and payers) across the entire development pathway.

Retrospective Analysis Using Pharmacokinetic/Pharmacodynamic Modeling and Simulation Offers Improvements in Efficiency of the Design of Volunteer Infection Studies for Antimalarial Drug Development.

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.

Model-Informed Drug Development for Malaria Therapeutics.

Malaria is a critical public health problem resulting in substantial morbidity and mortality, particularly in developing countries. Owing to the development of resistance toward current therapies, novel approaches to accelerate the development efforts of new malaria therapeutics are urgently needed. There have been significant advancements in the development of in vitro and in vivo experiments that generate data used to inform decisions about the potential merit of new compounds. A comprehensive disease-drug model capable of integrating discrete data from different preclinical and clinical components would be a valuable tool across all stages of drug development. This could have an enormous impact on the otherwise slow and resource-intensive process of traditional clinical drug development.