The implications of model-informed drug discovery and development for tuberculosis

Pharmacometrics in tuberculosis: progress and opportunities

Tuberculosis 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 employs 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 antituberculosis 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 multidrug regimens in terms of both efficacy and safety, informing regimen design for future study. In this short clinically-focused review, we explore what has been done, and what opportunities exist for pharmacometrics to impact tuberculosis pharmacotherapy.

Mechanistic modelling of enzyme-restoration effects for new recombinant liver-targeted proteins in acute intermittent porphyria

Background and Purpose

Acute intermittent porphyria (AIP) is a rare disease caused by a genetic mutation in the hepatic activity of the porphobilinogen‐deaminase. We aimed to develop a mechanistic model of the enzymatic restoration effects of a novel therapy based on the administration of different formulations of recombinant human‐PBGD (rhPBGD) linked to the ApoAI lipoprotein. This fusion protein circulates in blood, incorporating into HDL and penetrating hepatocytes.

Experimental Approach

Single i.v. dose of different formulations of rhPBGD linked to ApoAI were administered to AIP mice in which a porphyric attack was triggered by i.p. phenobarbital. Data consist on 24 h urine excreted amounts of heme precursors, 5‐aminolevulinic acid (ALA), PBG and total porphyrins that were analysed using non‐linear mixed‐effects analysis.

Key Results

The mechanistic model successfully characterized over time the amounts excreted in urine of the three heme precursors for different formulations of rhPBGD and unravelled several mechanisms in the heme pathway, such as the regulation in ALA synthesis by heme. Treatment with rhPBGD formulations restored PBGD activity, increasing up to 51 times the value of the rate of tPOR formation estimated from baseline. Model‐based simulations showed that several formulation prototypes provided efficient protective effects when administered up to 1 week prior to the occurrence of the AIP attack.

Conclusion and Implications

The model developed had excellent performance over a range of doses and formulation type. This mechanistic model warrants use beyond ApoAI‐conjugates and represents a useful tool towards more efficient drug treatments of other enzymopenias as well as for acute intermittent porphyria.

Bacterial growth dynamics and pharmacokinetic-pharmacodynamic relationships of rifampicin and bedaquiline in BALB/c mice

BACKGROUND AND PURPOSE

Translational efforts in the evaluation of novel anti-tubercular drugs demand better integration of pharmacokinetic-pharmacodynamic data arising from preclinical protocols. However, parametric approaches that discriminate drug effect from the underlying bacterial growth dynamics have not been fully explored, making it difficult to translate and/or extrapolate preclinical findings to humans. This analysis aims to develop a drug-disease model that allows distinction between drug- and system-specific properties.

EXPERIMENTAL APPROACH

Given their clinical relevance, rifampicin and bedaquiline were used as test compounds. A two-state model was used to describe bacterial growth dynamics. The approach assumes the existence of fast- and slow-growing bacterial populations. Drug effect on the growth dynamics of each subpopulation was characterised in terms of potency (EC₅₀ -F and EC₅₀ -S) and maximum killing rate.

KEY RESULTS

The doubling time of the fast- and slow-growing population was estimated to be 25 h and 42 days, respectively. Rifampicin was more potent against the fast-growing (EC₅₀ -F = 4.8 mg·L^-1 ), as compared with the slow-growing population (EC₅₀ -S = 60.2 mg·L^-1 ). Bedaquiline showed higher potency than rifampicin (EC₅₀ -F = 0.19 mg·L^-1 ; EC₅₀ -S = 3.04 mg·L^-1 ). External validation procedures revealed an effect of infection route on the apparent potency of rifampicin.

CONCLUSION AND IMPLICATIONS

Model parameter estimates suggest that nearly maximum killing rate is achieved against fast-growing, but not against slow-growing populations at the tested doses. Evidence of differences in drug potency for each subpopulation may facilitate the translation of preclinical findings and improve the dose rationale for anti-tubercular drugs in humans.

Early Drug Development and Evaluation of Putative Antitubercular Compounds in the -Omics Era

Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis. According to the WHO, the disease is one of the top 10 causes of death of people worldwide. Mycobacterium tuberculosis is an intracellular pathogen with an unusually thick, waxy cell wall and a complex life cycle. These factors, combined with M. tuberculosis ability to enter prolonged periods of latency, make the bacterium very difficult to eradicate. The standard treatment of TB requires 6-20months, depending on the drug susceptibility of the infecting strain. The need to take cocktails of antibiotics to treat tuberculosis effectively and the emergence of drug-resistant strains prompts the need to search for new antitubercular compounds. This review provides a perspective on how modern -omic technologies facilitate the drug discovery process for tuberculosis treatment. We discuss how methods of DNA and RNA sequencing, proteomics, and genetic manipulation of organisms increase our understanding of mechanisms of action of antibiotics and allow the evaluation of drugs. We explore the utility of mathematical modeling and modern computational analysis for the drug discovery process. Finally, we summarize how -omic technologies contribute to our understanding of the emergence of drug resistance.

How long will treatment guidelines for TB continue to overlook variability in drug exposure?

BACKGROUND

Despite wide clinical acceptance, the use of weight-banded dosing regimens for the treatment of TB in adults has been defined on an empirical basis. The potential impact of known covariate factors on exposure to different drugs has not been taken into account.

OBJECTIVES

To evaluate the effect of demographic factors on the exposure to standard of care drugs after weight-banded dosing, as currently recommended by TB treatment guidelines. In addition, we aim to identify alternative dosing regimens that ensure comparable systemic exposure across the overall patient population.

METHODS

Clinical trial simulations were performed to assess the differences in systemic exposure in a cohort of virtual patients. Secondary pharmacokinetic parameters were used to evaluate the adequacy of each regimen along with the percentage of patients achieving predefined thresholds.

RESULTS

Our results show that patients weighing less than 40 kg are underexposed relative to patients with higher body weight. The opposite trend was observed following a crude weight band-based dosing regimen with 50 kg as the cut-off point. Simulations indicate that a fixed-dose regimen based on three (<40 kg), four (40-70 kg) or five (>70 kg) tablets of 150 mg rifampicin, 75 mg isoniazid, 400 mg pyrazinamide and 275 mg ethambutol reduces variability in exposure, increasing the overall probability of favourable long-term outcome across the population.

CONCLUSIONS

These findings suggest the need to revisit current guidelines for the dose of standard of care drugs for TB treatment in adults. The proposed fixed-dose regimen should be considered in future clinical trials.

Evaluation of pharmacokinetic-pharmacodynamic relationships and selection of drug combinations for tuberculosis

AIMS

Despite evidence of the efficacy of anti-tubercular drug regimens in clinical practice, the rationale underpinning the selection of doses and companion drugs for combination therapy remains empirical. Novel methods are needed to optimise the antibacterial activity in combination therapies. A drug-disease modelling framework for rational selection of dose and drug combinations in tuberculosis is presented here.

METHODS

A model-based meta-analysis was performed to assess the antibacterial activity of different combinations in infected mice. Data retrieved from the published literature were analysed using a two-state bacterial growth dynamics model, including fast- and slow-growing bacterial populations. The contribution of each drug to the overall antibacterial activity of the combination was parameterised as relative change to the potency of the backbone drug (EC₅₀ -F and/or EC₅₀ -S). Rifampicin and bedaquiline were selected as paradigm drugs to evaluate the predictive performance of the modelling approach.

RESULTS

Pyrazinamide increased the potency (EC₅₀ -F and EC₅₀ -S) of rifampicin (RZ) and bedaquiline (BZ) by almost two-fold. By contrast, pretomanid and isoniazid were found to worsen the antibacterial activity of BZ and RZ, respectively. Following extrapolation of in vivo pharmacokinetic-pharmacodynamic relationships, the dose of rifampicin showing maximum bactericidal effect in tuberculosis patients was predicted to be 70 mg·kg^-1 when given in combination with pyrazinamide.

CONCLUSIONS

The use of a drug-disease modelling framework may provide a more robust rationale for extrapolation and selection of dose and companion drugs in humans. Our analysis demonstrates that RZ and BZ should be considered as a backbone therapy in prospective novel combination regimens against tuberculosis.

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

Mimicking in-vivo exposures to drug combinations in-vitro: anti-tuberculosis drugs in lung lesions and the hollow fiber model of infection

Here, we evaluate protocol requirements to mimic therapeutically relevant drug concentrations at the site of infection (i.e. lung lesion) in an in-vitro hollow fibre model of infection using pulmonary tuberculosis as a paradigm. Steady-state pharmacokinetic profiles in plasma, lung tissue and lung lesion homogenate were simulated for isoniazid, rifampicin and pyrazinamide and moxifloxacin. An R-shiny User Interface was developed to support conversion of in-vivo pharmacokinetic C_MAX, T_MAX and T_1/2 estimates into pump settings. A monotherapy protocol mimicking isoniazid in lung lesion homogenate (isoniazid C_MAX = 1,200 ng/ml, T_MAX = 2.2 hr and T_1/2 = 4.7 hr), and two combination therapy protocols including drugs with similar (isoniazid and rifampicin (C_MAX = 400 ng/ml)) and different half-lives (isoniazid and pyrazinamide (C_MAX = 28,900 ng/ml and T_1/2 = 8.0 hr)) were implemented in a hollow-fiber system. Drug levels in the perfusate were analysed using ultra-high-performance liquid chromatographic-tandem mass spectrometric detection. Steady state pharmacokinetic profiles measured in the hollow fiber model were similar to the predicted in-vivo steady-state lung lesion homogenate pharmacokinetic profiles. The presented approach offers the possibility to use pharmacological data to study the effect of target tissue exposure for drug combinations. Integration with pharmacokinetics modelling principles through a web interface will provide access to a wider community interested in the evaluation of efficacy of anti-tubercular drugs.

PlatformTM, a standards-based data custodianship platform for translational medicine research

Biomedical informatics has traditionally adopted a linear view of the informatics process (collect, store and analyse) in translational medicine (TM) studies; focusing primarily on the challenges in data integration and analysis. However, a data management challenge presents itself with the new lifecycle view of data emphasized by the recent calls for data re-use, long term data preservation, and data sharing. There is currently a lack of dedicated infrastructure focused on the 'manageability' of the data lifecycle in TM research between data collection and analysis. Current community efforts towards establishing a culture for open science prompt the creation of a data custodianship environment for management of TM data assets to support data reuse and reproducibility of research results. Here we present the development of a lifecycle-based methodology to create a metadata management framework based on community driven standards for standardisation, consolidation and integration of TM research data. Based on this framework, we also present the development of a new platform (PlatformTM) focused on managing the lifecycle for translational research data assets.

Pharmacokinetic-Pharmacodynamic modelling of intracellular Mycobacterium tuberculosis growth and kill rates is predictive of clinical treatment duration

Tuberculosis (TB) treatment is long and complex, typically involving a combination of drugs taken for 6 months. Improved drug regimens to shorten and simplify treatment are urgently required, however a major challenge to TB drug development is the lack of predictive pre-clinical tools. To address this deficiency, we have adopted a new high-content imaging-based approach capable of defining the killing kinetics of first line anti-TB drugs against intracellular Mycobacterium tuberculosis (Mtb) residing inside macrophages. Through use of this pharmacokinetic-pharmacodynamic (PK-PD) approach we demonstrate that the killing dynamics of the intracellular Mtb sub-population is critical to predicting clinical TB treatment duration. Integrated modelling of intracellular Mtb killing alongside conventional extracellular Mtb killing data, generates the biphasic responses typical of those described clinically. Our model supports the hypothesis that the use of higher doses of rifampicin (35 mg/kg) will significantly reduce treatment duration. Our described PK-PD approach offers a much needed decision making tool for the identification and prioritisation of new therapies which have the potential to reduce TB treatment duration.