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

Economic implications of novel regimens for tuberculosis treatment in three high-burden countries: a modelling analysis

BACKGROUND

With numerous trials investigating novel drug combinations to treat tuberculosis, we aimed to evaluate the extent to which future improvements in tuberculosis treatment regimens could offset potential increases in drug costs.

METHODS

In this modelling analysis, we used an ingredients-based approach to estimate prices at which novel regimens for rifampin-susceptible and rifampin-resistant tuberculosis treatment would be cost-neutral or cost-effective compared with standards of care in India, the Philippines, and South Africa. We modelled regimens meeting targets set in the WHO's 2023 Target Regimen Profiles (TRPs). Our decision-analytical model tracked cohorts of adults initiating rifampin-susceptible or rifampin-resistant tuberculosis treatment, simulating their health outcomes and costs accumulated during and following treatment under standard-of-care and novel regimen scenarios. Price thresholds included short-term cost-neutrality (considering only savings accrued during treatment), medium-term cost-neutrality (additionally considering savings from averted retreatments and secondary cases), and cost-effectiveness (incorporating willingness-to-pay for improved health outcomes).

FINDINGS

Total medium-term costs per person treated using standard-of-care regimens were estimated at US$450 (95% uncertainty interval 310-630) in India, $560 (350-860) in the Philippines, and $730 (530-1090) in South Africa for rifampin-susceptible tuberculosis (current drug costs $46) and $2100 (1590-2810) in India, $2610 (2090-3280) in the Philippines, and $3790 (3090-4630) in South Africa for rifampin-resistant tuberculosis (current drug costs $432). A rifampin-susceptible tuberculosis regimen meeting the optimal targets defined in the TRPs could be cost-neutral in the short term at drug costs of $140 (90-210) per full course in India, $230 (130-380) in the Philippines, and $280 (180-460) in South Africa. For rifampin-resistant tuberculosis, short-term cost-neutral thresholds were higher with $930 (720-1230) in India, $1180 (980-1430) in the Philippines, and $1480 (1230-1780) in South Africa. Medium-term cost-neutral prices were approximately $50-100 higher than short-term cost-neutral prices for rifampin-susceptible tuberculosis and $250-550 higher for rifampin-resistant tuberculosis. Health system cost-neutral prices that excluded patient-borne costs were 45-70% lower (rifampin-susceptible regimens) and 15-50% lower (rifampin-resistant regimens) than the cost-neutral prices that included patient costs. Cost-effective prices were substantially higher. Shorter duration was the most important driver of medium-term savings with novel regimens, followed by ease of adherence.

INTERPRETATION

Improved tuberculosis regimens, particularly shorter regimens or those that facilitate better adherence, could reduce overall costs, potentially offsetting higher prices.

FUNDING

WHO.

Assessing efficacy in non-inferiority trials with non-adherence to interventions: Are intention-to-treat and per-protocol analyses fit for purpose?

BACKGROUND

Non-inferiority trials comparing different active drugs are often subject to treatment non-adherence. Intention-to-treat (ITT) and per-protocol (PP) analyses have been advocated in such studies but are not guaranteed to be unbiased in the presence of differential non-adherence.

METHODS

The REMoxTB trial evaluated two 4-month experimental regimens compared with a 6-month control regimen for newly diagnosed drug-susceptible TB. The primary endpoint was a composite unfavorable outcome of treatment failure or recurrence within 18 months post-randomization. We conducted a simulation study based on REMoxTB to assess the performance of statistical methods for handling non-adherence in non-inferiority trials, including: ITT and PP analyses, adjustment for observed adherence, multiple imputation (MI) of outcomes, inverse-probability-of-treatment weighting (IPTW), and a doubly-robust (DR) estimator.

RESULTS

When non-adherence differed between trial arms, ITT, and PP analyses often resulted in non-trivial bias in the estimated treatment effect, which consequently under- or over-inflated the type I error rate. Adjustment for observed adherence led to similar issues, whereas the MI, IPTW and DR approaches were able to correct bias under most non-adherence scenarios; they could not always eliminate bias entirely in the presence of unobserved confounding. The IPTW and DR methods were generally unbiased and maintained desired type I error rates and statistical power.

CONCLUSIONS

When non-adherence differs between trial arms, ITT and PP analyses can produce biased estimates of efficacy, potentially leading to the acceptance of inferior treatments or efficacious regimens being missed. IPTW and the DR estimator are relatively straightforward methods to supplement ITT and PP approaches.

Model-Informed Precision Dosing of Isoniazid: Parametric Population Pharmacokinetics Model Repository

Introduction

Isoniazid (INH) is a crucial first-line anti tuberculosis (TB) drug used in adults and children. However, various factors can alter its pharmacokinetics (PK). This article aims to establish a population pharmacokinetic (popPK) models repository of INH to facilitate clinical use.

Methods

A literature search was conducted until August 23, 2022, using PubMed, Embase, and Web of Science databases. We excluded published popPK studies that did not provide full model parameters or used a non-parametric method. Monte Carlo simulation works was based on RxODE. The popPK models repository was established using R. Non-compartment analysis was based on IQnca.

Results

Fourteen studies included in the repository, with eleven studies conducted in adults, three studies in children, one in pregnant women. Two-compartment with allometric scaling models were commonly used as structural models. NAT2 acetylator phenotype significantly affecting the apparent clearance (CL). Moreover, postmenstrual age (PMA) influenced the CL in pediatric patients. Monte Carlo simulation results showed that the geometric mean ratio (95% Confidence Interval, CI) of PK parameters in most studies were within the acceptable range (50.00-200.00%), pregnant patients showed a lower exposure. After a standard treatment strategy, there was a notable exposure reduction in the patients with the NAT2 RA or nonSA (IA/RA) phenotype, resulting in a 59.5% decrease in AUC0-24 and 83.2% decrease in Cmax (Infants), and a 49.3% reduction in AUC0-24 and 73.5% reduction in Cmax (Adults).

Discussion

Body weight and NAT2 acetylator phenotype are the most significant factors affecting the exposure of INH. PMA is a crucial factor in the pediatric population. Clinicians should consider these factors when implementing model-informed precision dosing of INH. The popPK model repository for INH will aid in optimizing treatment and enhancing patient outcomes.

Lead identification against Mycobacterium tuberculosis using highly enriched active molecules against pantothenate synthetase

The Pantothenate synthetase (PS) from the Mycobacterium tuberculosis (Mtb) holds a crucial role in the survival and robust proliferation of bacteria through its catalysis of coenzyme A and acyl carrier protein synthesis. The present study undertook the PS drug target in complex with a co-crystallized ligand and subjected it to docking and virtual screening approaches. The experimental design encompassed three discrete datasets: an active dataset featuring 136 compounds, an inactive dataset comprising 56 compounds, and a decoys dataset curated from the zinc library, comprising an extensive compilation of approximately 53,000 compounds. The compounds' binding energies were observed to be in the range of -5 to ∼-14 kcal/mol. Additionally, binding energy results were further refined through Enrichment Factor analysis (EF). EF is a new statistical approach which uses the scores obtained from docking-based virtual screening and predicts the precision of the scoring function. Remarkably, the Enrichment Factor (EF) analysis produced exceptionally favorable outcomes, attaining an EF of approximately 49% within the uppermost 1% fraction of the compound distribution. Finally, a total of eight compounds, evenly distributed between the active dataset and the decoys dataset, emerged as potent inhibitors of the Pantothenate synthetase (PS) enzyme. The analysis of inhibition constants and binding energy revealed a notable correlation, with an r-squared value (r2) of 0.912 between the two parameters. Furthermore, the shortlisted compounds were subjected to 100 ns MD simulation to determine their stability and dynamics behavior. The decoy compounds that have been identified, exhibiting properties comparable to the active compounds, are postulated as potential candidates for targeting the Pantothenate synthetase (PS) enzyme to treat Mtb infection. Nevertheless, in the pursuit of a comprehensive investigation, it is advisable to undertake additional experimental validation as a component of the subsequent study.Communicated by Ramaswamy H. Sarma.

Pharmacokinetic analysis across studies to drive knowledge-integration: A tutorial on individual patient data meta-analysis (IPDMA)

Answering challenging questions in drug development sometimes requires pharmacokinetic (PK) data analysis across different studies, for example, to characterize PKs across diverse regions or populations, or to increase statistical power for subpopulations by combining smaller size trials. Given the growing interest in data sharing and advanced computational methods, knowledge integration based on multiple data sources is increasingly applied in the context of model-informed drug discovery and development. A powerful analysis method is the individual patient data meta-analysis (IPDMA), leveraging systematic review of databases and literature, with the most detailed data type of the individual patient, and quantitative modeling of the PK processes, including capturing heterogeneity of variance between studies. The methodology that should be used in IPDMA in the context of population PK analysis is summarized in this tutorial, highlighting areas of special attention compared to standard PK modeling, including hierarchical nested variability terms for interstudy variability, and handling between-assay differences in limits of quantification within a single analysis. This tutorial is intended for any pharmacological modeler who is interested in performing an integrated analysis of PK data across different studies in a systematic and thorough manner, to answer questions that transcend individual primary studies.

QcrB inhibition as a potential approach for the treatment of tuberculosis: A review of recent developments, patents, and future directions

The unmet medical need for drug-resistant tuberculosis (DRTB) is a significant concern. Accordingly, identifying new drug targets for tuberculosis (TB) treatment and developing new therapies based on these drug targets is one of the strategies to tackle DRTB. QcrB is an innovative drug target to create treatments for DRTB. This article highlights QcrB inhibitors and their therapeutic compositions for treating TB. The literature for this article was gathered from PubMed and free patent databases utilizing different keywords related to QcrB inhibitor-based inventions. The data was collected from the conceptualization of telacebec (2010) QcrB to December 2022. A little interesting and encouraging research has been performed on QcrB inhibitors. Telacebec and TB47 are established QcrB inhibitors in the clinical trial. The inventive QcrB inhibitor-based drug combinations can potentially handle DRTB and reduce the TB therapy duration. The authors anticipate great opportunities in fostering QcrB inhibitor-based patentable pharmaceutical inventions against TB. Drug repurposing can be a promising strategy to get safe and effective QcrB inhibitors. However, developing drug resistance, drug tolerance, and selectivity of QcrB inhibitors for Mtb will be the main challenges in developing effective QcrB inhibitors. In conclusion, QcrB is a promising drug target for developing effective treatments for active, latent, and drug-resistant TB. Many inventive and patentable combinations and compositions of QcrB inhibitors with other anti-TB drugs are anticipated as future treatments for TB.

Advances in the design of combination therapies for the treatment of tuberculosis

INTRODUCTION

Tuberculosis requires lengthy multi-drug therapy. Mycobacterium tuberculosis occupies different tissue compartments during infection, making drug access and susceptibility patterns variable. Antibiotic combinations are needed to ensure each compartment of infection is reached with effective drug treatment. Despite drug combinations' role in treating tuberculosis, the design of such combinations has been tackled relatively late in the drug development process, limiting the number of drug combinations tested. In recent years, there has been significant progress using in vitro, in vivo, and computational methodologies to interrogate combination drug effects.

AREAS COVERED

This review discusses the advances in these methodologies and how they may be used in conjunction with new successful clinical trials of novel drug combinations to design optimized combination therapies for tuberculosis. Literature searches for approaches and experimental models used to evaluate drug combination effects were undertaken.

EXPERT OPINION

We are entering an era richer in combination drug effect and pharmacokinetic/pharmacodynamic data, genetic tools, and outcome measurement types. Application of computational modeling approaches that integrate these data and produce predictive models of clinical outcomes may enable the field to generate novel, effective multidrug therapies using existing and new drug combination backbones.

Can digital adherence technologies reduce inequity in tuberculosis treatment success? Evidence from a randomised controlled trial

INTRODUCTION

Tuberculosis (TB) is a global health emergency and low treatment adherence among patients is a major barrier to ending the TB epidemic. The WHO promotes digital adherence technologies (DATs) as facilitators for improving treatment adherence in resource-limited settings. However, limited research has investigated whether DATs improve outcomes for high-risk patients (ie, those with a high probability of an unsuccessful outcome), leading to concerns that DATs may cause intervention-generated inequality.

METHODS

We conducted secondary analyses of data from a completed individual-level randomised controlled trial in Nairobi, Kenya during 2016-2017, which evaluated the average intervention effect of a novel DAT-based behavioural support programme. We trained a causal forest model to answer three research questions: (1) Was the effect of the intervention heterogeneous across individuals? (2) Was the intervention less effective for high-risk patients? nd (3) Can differentiated care improve programme effectiveness and equity in treatment outcomes?

RESULTS

We found that individual intervention effects-the percentage point reduction in the likelihood of an unsuccessful treatment outcome-ranged from 4.2 to 12.4, with an average of 8.2. The intervention was beneficial for 76% of patients, and most beneficial for high-risk patients. Differentiated enrolment policies, targeted at high-risk patients, have the potential to (1) increase the average intervention effect of DAT services by up to 28.5% and (2) decrease the population average and standard deviation (across patients) of the probability of an unsuccessful treatment outcome by up to 8.5% and 31.5%, respectively.

CONCLUSION

This DAT-based intervention can improve outcomes among high-risk patients, reducing inequity in the likelihood of an unsuccessful treatment outcome. In resource-limited settings where universal provision of the intervention is infeasible, targeting high-risk patients for DAT enrolment is a worthwhile strategy for programmes that involve human support sponsors, enabling them to achieve the highest possible impact for high-risk patients at a substantially improved cost-effectiveness ratio.

Introduction to the Theme "New Insights, Strategies, and Therapeutics for Common Diseases"

The reviews in Volume 62 of the Annual Review of Pharmacology and Toxicology (ARPT) cover a diverse range of topics. A theme that encompasses many of these reviews is their relevance to common diseases and disorders, including type 2 diabetes, heart failure, cancer, tuberculosis, Alzheimer's disease, neurodegenerative disorders, and Down syndrome. Other reviews highlight important aspects of therapeutics, including placebos and patient-centric approaches to drug formulation. The reviews with this thematic focus, as well as other reviews in this volume, emphasize new mechanistic insights, experimental and therapeutic strategies, and novel insights regarding topics in the disciplines of pharmacology and toxicology. As the editors of ARPT, we believe that these reviews help advance those disciplines and, even more importantly, have the potential to improve the health care of the world's population. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.