Translational predictions of phase 2a first-in-patient efficacy studies for antituberculosis drugs.

BACKGROUND: Phase 2a trials in tuberculosis typically use early bactericidal activity (EBA), the decline in sputum CFU over 14 days, as the primary end-point for testing the efficacy of drugs as monotherapy. However, the cost of phase 2a trials can range from USD 7 million to USD 19.6 million on average, while >30% of drugs fail to progress to phase 3. Better utilising pre-clinical data to predict and prioritise the most likely drugs to succeed will thus help to accelerate drug development and reduce costs. We aim to predict clinical EBA using pre-clinical in vivo pharmacokinetic (PK)-pharmacodynamic (PD) data and a model-based translational pharmacology approach. METHODS AND FINDINGS: First, mouse PK, PD and clinical PK models were compiled. Second, mouse PK-PD models were built to derive an exposure-response relationship. Third, translational prediction of clinical EBA studies was performed using mouse PK-PD relationships and informed by clinical PK models and species-specific protein binding. Presence or absence of clinical efficacy was accurately predicted from the mouse model. Predicted daily decreases of CFU in the first 2 days of treatment and between day 2 and day 14 were consistent with clinical observations. CONCLUSION: This platform provides an innovative solution to inform or even replace phase 2a EBA trials, to bridge the gap between mouse efficacy studies and phase 2b and phase 3 trials, and to substantially accelerate drug development.

Translational predictions of phase 2a first-in-patient efficacy studies for antituberculosis drugs.

Background: Phase 2a trials in tuberculosis typically use early bactericidal activity (EBA), the decline in sputum colony forming units (CFU) over 14 days, as the primary outcome for testing the efficacy of drugs as monotherapy. However, the cost of phase 2a trials can range from 7 to 19.6 million dollars on average, while more than 30% of drugs fail to progress to phase 3. Better utilizing preclinical data to predict and prioritize the most likely drugs to succeed will thus help accelerate drug development and reduce costs. We aim to predict clinical EBA using preclinical in vivo pharmacokinetic-pharmacodynamic (PKPD) data and a model-based translational pharmacology approach. Methods and Findings: First, mouse PK, PD and clinical PK models were compiled. Second, mouse PKPD models were built to derive an exposure response relationship. Third, translational prediction of clinical EBA studies was performed using mouse PKPD relationships and informed by clinical PK models and species-specific protein binding. Presence or absence of clinical efficacy was accurately predicted from the mouse model. Predicted daily decreases of CFU in the first 2 days of treatment and between day 2 and day 14 were consistent with clinical observations. Conclusion: This platform provides an innovative solution to inform or even replace phase 2a EBA trials, to bridge the gap between mouse efficacy studies and phase 2b and phase 3 trials, and to substantially accelerate drug development.

Pharmacometrics in tuberculosis: progress and opportunities.

Tuberculosis (TB) remains one of the leading causes of death by a communicable agent, infecting up to one-quarter of the world's population, predominantly in disadvantaged communities. Pharmacometrics employ quantitative mathematical models to describe the relationships between pharmacokinetics and pharmacodynamics, and to predict drug doses, exposures and responses. Pharmacometric approaches have provided a scientific basis for improved dosing of anti-TB drugs and concomitantly administered antiretrovirals at the population level. The development of modelling frameworks including physiologically based pharmacokinetics, quantitative systems pharmacology and machine learning provides an opportunity to extend the role of pharmacometrics to in-silico quantification of drug-drug interactions, prediction of doses for special populations, dose optimization and individualization, and understanding the complex exposure-response relationships of multi-drug regimens in terms of both efficacy and safety, informing regimen design for future study. This short, clinically focused review explores what has been done, and what opportunities exist for pharmacometrics to impact TB pharmacotherapy.

A Rabbit Model to Study Antibiotic Penetration at the Site of Infection for Nontuberculous Mycobacterial Lung Disease: Macrolide Case Study.

Nontuberculous mycobacterial pulmonary disease (NTM-PD) is a potentially fatal infectious disease requiring long treatment duration with multiple antibiotics and against which there is no reliable cure. Among the factors that have hampered the development of adequate drug regimens is the lack of an animal model that reproduces the NTM lung pathology required for studying antibiotic penetration and efficacy. Given the documented similarities between tuberculosis and NTM immunopathology in patients, we first determined that the rabbit model of active tuberculosis reproduces key features of human NTM-PD and provides an acceptable surrogate model to study lesion penetration. We focused on clarithromycin, a macrolide and pillar of NTM-PD treatment, and explored the underlying causes of the disconnect between its favorable potency and pharmacokinetics and inconsistent clinical outcome. To quantify pharmacokinetic-pharmacodynamic target attainment at the site of disease, we developed a translational model describing clarithromycin distribution from plasma to lung lesions, including the spatial quantitation of clarithromycin and azithromycin in mycobacterial lesions of two patients on long-term macrolide therapy. Through clinical simulations, we visualized the coverage of clarithromycin in plasma and four disease compartments, revealing heterogeneous bacteriostatic and bactericidal target attainment depending on the compartment and the corresponding potency against nontuberculous mycobacteria in clinically relevant assays. Overall, clarithromycin's favorable tissue penetration and lack of bactericidal activity indicated that its clinical activity is limited by pharmacodynamic, rather than pharmacokinetic, factors. Our results pave the way toward the simulation of lesion pharmacokinetic-pharmacodynamic coverage by multidrug combinations to enable the prioritization of promising regimens for clinical trials.

Comparative Efficacy of Rifapentine Alone and in Combination with Isoniazid for Latent Tuberculosis Infection: a Translational Pharmacokinetic-Pharmacodynamic Modeling Study.

Rifapentine has facilitated treatment shortening for latent tuberculosis infection (LTBI) in combination with isoniazid once weekly for 3 months (3HP) or daily for 1 month (1HP). Our objective was to determine the optimal rifapentine dose for a 6-week monotherapy regimen (6wP) and predict clinical efficacy. Rifapentine and isoniazid pharmacokinetics were simulated in mice and humans. Mouse lung CFU data were used to characterize exposure-response relationships of 1HP, 3HP, and 6wP and translated to predict clinical efficacy. A 600-mg daily dose for 6wP delivered greater cumulative rifapentine exposure than 1HP or 3HP. The maximum regimen effect (Emax) was 0.24 day-1. The regimen potencies, measured as the concentration at 50% of Emax (EC50), were estimated to be 2.12 mg/liter for 3HP, 3.72 mg/liter for 1HP, and 4.71 mg/liter for 6wP, suggesting that isoniazid contributes little to 1HP efficacy. Clinical translation predicted that 6wP reduces bacterial loads at a higher rate than 3HP and to a greater extent than 3HP and 1HP. 6wP (600 mg daily) is predicted to result in equal or better efficacy than 1HP and 3HP for LTBI treatment without the potential added toxicity of isoniazid. Results from ongoing and future clinical studies will be required to support these findings.

Lesion Penetration and Activity Limit the Utility of Second-Line Injectable Agents in Pulmonary Tuberculosis.

Amikacin and kanamycin are second-line injectables used in the treatment of multidrug-resistant tuberculosis (MDR-TB) based on the clinical utility of streptomycin, another aminoglycoside and first-line anti-TB drug. While streptomycin was tested as a single agent in the first controlled TB clinical trial, introduction of amikacin and kanamycin into MDR-TB regimens was not preceded by randomized controlled trials. A recent large retrospective meta-analysis revealed that compared with regimens without any injectable drug, amikacin provided modest benefits, and kanamycin was associated with worse outcomes. Although their long-term use can cause irreversible ototoxicity, they remain part of MDR-TB regimens because they have a role in preventing emergence of resistance to other drugs. To quantify the contribution of amikacin and kanamycin to second-line regimens, we applied two-dimensional matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging in large lung lesions, quantified drug exposure in lung and in lesions of rabbits with active TB, and measured the concentrations required to kill or inhibit growth of the resident bacterial populations. Using these metrics, we applied site-of-action pharmacokinetic and pharmacodynamic (PK-PD) concepts and simulated drug coverage in patients' lung lesions. The results provide a pharmacological explanation for the limited clinical utility of both agents and reveal better PK-PD lesion coverage for amikacin than kanamycin, consistent with retrospective data of contribution to treatment success. Together with recent mechanistic studies dissecting antibacterial activity from aminoglycoside ototoxicity, the limited but rapid penetration of streptomycin, amikacin, and kanamycin to the sites of TB disease supports the development of analogs with improved efficacy and tolerability.

Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis.

Tuberculosis (TB) kills more people than any other infectious disease. Challenges for developing better treatments include the complex pathology due to within-host immune dynamics, interpatient variability in disease severity and drug pharmacokinetics-pharmacodynamics (PK-PD), and the growing emergence of resistance. Model-informed drug development using quantitative and translational pharmacology has become increasingly recognized as a method capable of drug prioritization and regimen optimization to efficiently progress compounds through TB drug development phases. In this review, we examine translational models and tools, including plasma PK scaling, site-of-disease lesion PK, host-immune and bacteria interplay, combination PK-PD models of multidrug regimens, resistance formation, and integration of data across nonclinical and clinical phases.We propose a workflow that integrates these tools with computational platforms to identify drug combinations that have the potential to accelerate sterilization, reduce relapse rates, and limit the emergence of resistance.

Optimizing Hydroxychloroquine Dosing for Patients With COVID-19: An Integrative Modeling Approach for Effective Drug Repurposing.

Hydroxychloroquine (HCQ) is a promising candidate for Coronavirus disease of 2019 (COVID-19) treatment. The optimal dosing of HCQ is unknown. Our goal was to integrate historic and emerging pharmacological and toxicity data to understand safe and efficacious HCQ dosing strategies for COVID-19 treatment. The data sources included were (i) longitudinal clinical, pharmacokinetic (PK), and virologic data from patients with severe acute respiratory syndrome-2 (SARS-CoV-2) infection who received HCQ with or without azithromycin (n = 116), (ii) in vitro viral replication data and SARS-CoV-2 viral load inhibition by HCQ, (iii) a population PK model of HCQ, and (iv) a model relating chloroquine PKs to corrected QT (QTc) prolongation. A mechanistic PK/virologic/QTc model for HCQ was developed and externally validated to predict SARS-CoV-2 rate of viral decline and QTc prolongation. SARS-CoV-2 viral decline was associated with HCQ PKs (P < 0.001). The extrapolated patient half-maximal effective concentration (EC50 ) was 4.7 µM, comparable to the reported in vitro EC50s . HCQ doses > 400 mg b.i.d. for ≥5 days were predicted to rapidly decrease viral loads, reduce the proportion of patients with detectable SARS-CoV-2 infection, and shorten treatment courses, compared with lower dose (≤ 400 mg daily) regimens. However, HCQ doses > 600 mg b.i.d. were also predicted to prolong QTc intervals. This prolongation may have clinical implications warranting further safety assessment. Due to COVID-19's variable natural history, lower dose HCQ regimens may be indistinguishable from controls. Evaluation of higher HCQ doses is needed to ensure adequate safety and efficacy.