Advancing pharmacometrics in Africa-Transition from capacity development toward job creation.

Trained pharmacometricians remain scarce in Africa due to limited training opportunities, lack of a pharmaceutical product development ecosystem, and emigration to high-income countries. The Applied Pharmacometrics Training (APT) fellowship program was established to address these gaps and specifically foster job creation for talent retention. We review the APT program's progress over 3 years and encourage collaboration to enhance local clinical data analysis in Africa.

Pharmacometric Analysis to Describe Pharmacokinetics and Exposure-Efficacy Response of Ivermectin in Adolescents Infected with Trichuris trichiura.

The efficacy of the combination therapy of albendazole and ivermectin against Trichuris trichiura infection is higher in Tanzania than in Côte d'Ivoire. This study therefore aimed to investigate the difference between the population pharmacokinetics (PK) at these study sites and to determine if an exposure-response analysis could explain the low efficacy of the combination therapy in Côte d'Ivoire. Twenty-four participants (aged 12-19 years) receiving single doses of ivermectin (200 µg/kg) and albendazole (400 mg) were included in the population PK modeling.

A Modern Curriculum for Training Scientists in Model-Informed Drug Development: Progress Report on FDA Grant to Train Regulatory Scientists.

Under US Food and Drug Administration (FDA) grant (2U18FD005320-06), the Critical Path Institute (C-Path) and experienced private sector partners collaborated with global health organizations to create didactic video materials in an e-learning format on model-informed drug development (MIDD) topics relevant to a non-modeling audience. Several multinational pharmaceutical companies contributed case studies illustrating the application of the MIDD approach in practice. Training videos were created and divided into several modules: introducing the MIDD landscape for drug development and regulatory science, a review of various model types used for MIDD, discussions of how models inform drug development and regulatory decisions, future goals of MIDD, and discussions on the interconnectedness of models used for MIDD.

Machine-Learning Assisted Screening of Correlated Covariates: Application to Clinical Data of Desipramine.

Stepwise covariate modeling (SCM) has a high computational burden and can select the wrong covariates. Machine learning (ML) has been proposed as a screening tool to improve the efficiency of covariate selection, but little is known about how to apply ML on actual clinical data. First, we simulated datasets based on clinical data to compare the performance of various ML and traditional pharmacometrics (PMX) techniques with and without accounting for highly-correlated covariates.

Emerging Therapeutics, Technologies, and Drug Development Strategies to Address Patient Nonadherence and Improve Tuberculosis Treatment.

Imperfect medication adherence remains the biggest predictor of treatment failure for patients with tuberculosis. Missed doses during treatment lead to relapse, tuberculosis resistance, and further spread of disease. Understanding individual patient phenotypes, population pharmacokinetics, resistance development, drug distribution to tuberculosis lesions, and pharmacodynamics at the site of infection is necessary to fully measure the impact of adherence on patient outcomes.

Needs-driven talent and competency development for the next generation of regulatory scientists in Africa.

Capacity building programmes for African regulators should link education, training and research with career development in an approach that combines an academic base and experiential learning aligned within a competency framework. A regulatory ecosystem that engages with a broad range of stakeholders will mean that expertise in the ever-expanding field of regulatory science filters into teaching and research in a symbiotic way. In this way capacity development interventions will be a collaborative approach between the learning context (academic and training institutions) and the performance context (regulatory agencies and industry), which will ultimately best serve the patients.

Traditional medicines for COVID-19: Perspectives from clinical pharmacologists.

There has been high interest in the use of traditional medicines for COVID-19 from early in the course of the pandemic. Significant advances in the science of ethnopharmacology have helped to introduce chemical entities identified from natural sources into modern medicine. However, the wider integration of natural products into the modern drug discovery process will require enhanced collaboration amongst the pharmaceutical industry, academic research units, regulatory bodies, ethics review committees and local, regional, continental and international organizations.

Clinical Pharmacology Worldwide: A Global Health Perspective.

Low- and middle-income countries (LMICs) have the highest rates of mortality and morbidity globally, but lag behind high-income countries in the number of clinical trials and trained researchers, as well as research data pertaining to their populations. Lack of local clinical pharmacology and pharmacometrics expertise, limited training opportunities, and lack of local genomic data may contribute to health inequalities and limit the application of precision medicine. Continuing to develop health care infrastructure, including well-designed clinical pharmacology training and data collection in LMICs, can help address these challenges.

Dolutegravir pharmacokinetics during co-administration with either artemether/lumefantrine or artesunate/amodiaquine.

Background: In sub-Saharan Africa, artemisinin-containing therapies for malaria treatment are regularly co-administered with ART. Currently, dolutegravir-based regimens are recommended as first-line therapy for HIV across most of Africa.

Objectives: To investigate the population pharmacokinetics of dolutegravir during co-administration with artemether/lumefantrine or artesunate/amodiaquine, two commonly used antimalarial therapies.

Risk reduction of diarrhea and respiratory infections following a community health education program - a facility-based case-control study in rural parts of Kenya.

Background: Diarrheal and acute respiratory infections remain a major cause of death in developing countries especially among children below 5 years of age. About 80% of all hospital attendances in Kenya can be attributed to preventable diseases and at least 50% of these preventable diseases are linked to poor sanitation. The purpose of this study was to assess the impact of a community-based health education program, called Familia Nawiri, in reducing the risk of diarrhea and respiratory infections among people living in three rural Kenyan communities.

Open-source mobile digital platform for clinical trial data collection in low-resource settings.

Background: Governments, universities and pan-African research networks are building durable infrastructure and capabilities for biomedical research in Africa. This offers the opportunity to adopt from the outset innovative approaches and technologies that would be challenging to retrofit into fully established research infrastructures such as those regularly found in high-income countries. In this context we piloted the use of a novel mobile digital health platform, designed specifically for low-resource environments, to support high-quality data collection in a clinical research study.

Variability in the population pharmacokinetics of pyrazinamide in South African tuberculosis patients.

Objective: This study was designed to characterize the population pharmacokinetics of pyrazinamide in South African pulmonary tuberculosis patients, with special reference to interindividual and interoccasional variability (IIV and IOV, respectively).

Methods: Concentration-time measurements obtained from 227 patients receiving oral doses of pyrazinamide were pooled to create a dataset containing 3,092 data points spanning multiple dosing occasions. The software program NONMEM was used to analyze the data.

Non-linear mixed effects modeling - from methodology and software development to driving implementation in drug development science.

Few scientific contributions have made significant impact unless there was a champion who had the vision to see the potential for its use in seemingly disparate areas-and who then drove active implementation. In this paper, we present a historical summary of the development of non-linear mixed effects (NLME) modeling up to the more recent extensions of this statistical methodology. The paper places strong emphasis on the pivotal role played by Lewis B.