Accelerating the transition of new tuberculosis drug combinations from Phase II to Phase III trials: New technologies and innovative designs

Developing biomarker assays to accelerate tuberculosis drug development: defining target product profiles

Drug development for tuberculosis is hindered by the methodological limitations in the definitions of patient outcomes, particularly the slow organism growth and difficulty in obtaining suitable and representative samples throughout the treatment. We developed target product profiles for biomarker assays suitable for early-phase and late-phase clinical drug trials by consulting subject-matter experts on the desirable performance and operational characteristics of such assays for monitoring of tuberculosis treatment in drug trials. Minimal and optimal criteria were defined for scope, intended use, pricing, performance, and operational characteristics of the biomarkers. Early-stage trial assays should accurately quantify the number of viable bacilli, whereas late-stage trial assays should match the number, predict relapse-free cure, and replace culture conversion endpoints. The operational criteria reflect the infrastructure and resources available for drug trials. The effective tools should define the sterilising activity of the drug and lower the probability of treatment failure or relapse in people with tuberculosis. The target product profiles outlined in this Review should guide and de-risk the development of biomarker-based assays suitable for phase 2 and 3 clinical drug trials.

Therapeutic developments for tuberculosis and nontuberculous mycobacterial lung disease

Tuberculosis (TB) drug discovery and development has undergone nothing short of a revolution over the past 20 years. Successful public-private partnerships and sustained funding have delivered a much-improved understanding of mycobacterial disease biology and pharmacology and a healthy pipeline that can tolerate inevitable attrition. Preclinical and clinical development has evolved from decade-old concepts to adaptive designs that permit rapid evaluation of regimens that might greatly shorten treatment duration over the next decade. But the past 20 years also saw the rise of a fatal and difficult-to-cure lung disease caused by nontuberculous mycobacteria (NTM), for which the drug development pipeline is nearly empty. Here, we discuss the similarities and differences between TB and NTM lung diseases, compare the preclinical and clinical advances, and identify major knowledge gaps and areas of cross-fertilization. We argue that applying paradigms and networks that have proved successful for TB, from basic research to clinical trials, will help to populate the pipeline and accelerate curative regimen development for NTM disease.

High rifampicin peak plasma concentrations accelerate the slow phase of bacterial decline in tuberculosis patients: Evidence for heteroresistance

BACKGROUND

Antibiotic treatments are often associated with a late slowdown in bacterial killing. This separates the killing of bacteria into at least two distinct phases: a quick phase followed by a slower phase, the latter of which is linked to treatment success. Current mechanistic explanations for the in vitro slowdown are either antibiotic persistence or heteroresistance. Persistence is defined as the switching back and forth between susceptible and non-susceptible states, while heteroresistance is defined as the coexistence of bacteria with heterogeneous susceptibilities. Both are also thought to cause a slowdown in the decline of bacterial populations in patients and therefore complicate and prolong antibiotic treatments. Reduced bacterial death rates over time are also observed within tuberculosis patients, yet the mechanistic reasons for this are unknown and therefore the strategies to mitigate them are also unknown.

METHODS AND FINDINGS

We analyse a dose ranging trial for rifampicin in tuberculosis patients and show that there is a slowdown in the decline of bacteria. We show that the late phase of bacterial killing depends more on the peak drug concentrations than the total drug exposure. We compare these to pharmacokinetic-pharmacodynamic models of rifampicin heteroresistance and persistence. We find that the observation on the slow phase's dependence on pharmacokinetic measures, specifically peak concentrations are only compatible with models of heteroresistance and incompatible with models of persistence. The quantitative agreement between heteroresistance models and observations is very good ([Formula: see text]). To corroborate the importance of the slowdown, we validate our results by estimating the time to sputum culture conversion and compare the results to a different dose ranging trial.

CONCLUSIONS

Our findings indicate that higher doses, specifically higher peak concentrations may be used to optimize rifampicin treatments by accelerating bacterial killing in the slow phase. It adds to the growing body of literature supporting higher rifampicin doses for shortening tuberculosis treatments.

Pharmacodynamics and Bactericidal Activity of Combination Regimens in Pulmonary Tuberculosis: Application to Bedaquiline-Pretomanid-Pyrazinamide

A critical barrier to codevelopment of tuberculosis (TB) regimens is a limited ability to identify optimal drug and dose combinations in early-phase clinical testing. While pharmacokinetic-pharmacodynamic (PKPD) target attainment is the primary tool for exposure-response optimization of TB drugs, the PD target is a static index that does not distinguish individual drug contributions to the efficacy of a multidrug combination. A PKPD model of bedaquiline-pretomanid-pyrazinamide (BPaZ) for the treatment of pulmonary TB was developed as part of a dynamic exposure-response approach to regimen development. The model describes a time course relationship between the drug concentrations in plasma and their individual as well as their combined effect on sputum bacillary load assessed by solid culture CFU counts and liquid culture time to positivity (TTP). The model parameters were estimated using data from the phase 2A studies NC-001-(J-M-Pa-Z) and NC-003-(C-J-Pa-Z). The results included a characterization of BPaZ activity as the most and least sensitive to changes in pyrazinamide and bedaquiline exposures, respectively, with antagonistic activity of BPa compensated by synergistic activity of BZ and PaZ. Simulations of the NC-003 study population with once-daily bedaquiline at 200 mg, pretomanid at 200 mg, and pyrazinamide at 1,500 mg showed BPaZ would require 3 months to attain liquid culture negativity in 90% of participants. These results for BPaZ were intended to be an example application with the general approach aimed at entirely novel drug combinations from a growing pipeline of new and repurposed TB drugs.

Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies

While synergistic drug combinations are more effective at fighting tumors with complex pathophysiology, preference compensating mechanisms, and drug resistance, the identification of novel synergistic drug combinations, especially complex higher-order combinations, remains challenging due to the size of combination space. Even though certain computational methods have been used to identify synergistic drug combinations in lieu of traditional in vitro and in vivo screening tests, the majority of previously published work has focused on predicting synergistic drug pairs for specific types of cancer and paid little attention to the sophisticated high-order combinations. The main objective of this study is to develop a deep learning-based approach that integrated multi-omics data to predict novel synergistic multi-drug combinations (DeepMDS) in a given cell line. To develop this approach, we firstly created a dataset comprising of gene expression profiles of cancer cell lines, target information of anti-cancer drugs, and drug response against a large variety of cancer cell lines. Based on the principle of a fully connected feed forward Deep Neural Network, the proposed model was constructed using this dataset, which achieved a high performance with a Mean Square Error (MSE) of 2.50 and a Root Mean Squared Error (RMSE) of 1.58 in the regression task, and gave the best classification accuracy of 0.94, an area under the Receiver Operating Characteristic curve (AUC) of 0.97, a sensitivity of 0.95, and a specificity of 0.93. Furthermore, we utilized three breast cancer cell subtypes (MCF-7, MDA-MD-468 and MDA-MB-231) and one lung cancer cell line A549 to validate the predicted results of our model, showing that the predicted top-ranked multi-drug combinations had superior anti-cancer effects to other combinations, particularly those that were widely used in clinical treatment. Our model has the potential to increase the practicality of expanding the drug combinational space and to leverage its capacity to prioritize the most effective multi-drug combinational therapy for precision oncology applications.

Anti-tuberculosis treatment strategies and drug development: challenges and priorities

Despite two decades of intensified research to understand and cure tuberculosis disease, biological uncertainties remain and hamper progress. However, owing to collaborative initiatives including academia, the pharmaceutical industry and non-for-profit organizations, the drug candidate pipeline is promising. This exceptional success comes with the inherent challenge of prioritizing multidrug regimens for clinical trials and revamping trial designs to accelerate regimen development and capitalize on drug discovery breakthroughs. Most wanted are markers of progression from latent infection to active pulmonary disease, markers of drug response and predictors of relapse, in vitro tools to uncover synergies that translate clinically and animal models to reliably assess the treatment shortening potential of new regimens. In this Review, we highlight the benefits and challenges of 'one-size-fits-all' regimens and treatment duration versus individualized therapy based on disease severity and host and pathogen characteristics, considering scientific and operational perspectives.

High Efficacy and Drug Synergy of HDAC6-Selective Inhibitor NN-429 in Natural Killer (NK)/T-Cell Lymphoma

NK/T-cell lymphoma (NKTCL) and γδ T-cell non-Hodgkin lymphomas (γδ T-NHL) are highly aggressive lymphomas that lack rationally designed therapies and rely on repurposed chemotherapeutics from other hematological cancers. Histone deacetylases (HDACs) have been targeted in a range of malignancies, including T-cell lymphomas. This study represents exploratory findings of HDAC6 inhibition in NKTCL and γδ T-NHL through a second-generation inhibitor NN-429. With nanomolar in vitro HDAC6 potency and high in vitro and in cellulo selectivity for HDAC6, NN-429 also exhibited long residence time and improved pharmacokinetic properties in contrast to older generation inhibitors. Following unique selective cytotoxicity towards γδ T-NHL and NKTCL, NN-429 demonstrated a synergistic relationship with the clinical agent etoposide and potential synergies with doxorubicin, cytarabine, and SNS-032 in these disease models, opening an avenue for combination treatment strategies.

Comparing timelines and evidence available to support new TB, HIV, and HCV drug approvals: The same, only different

Background

Tuberculosis (TB), human immunodeficiency virus (HIV), and hepatitis C virus (HCV) share a global presence and propensity to disproportionately affect marginalized populations. However, over recent decades, many fewer drugs have been brought to market for TB than for the others. Although three new anti-TB drugs have been approved in the US or Europe in the last 10 years, uptake of these drugs has been limited. Using case examples of drugs developed recently for TB, HIV, and HCV, we explore possible reasons. We examine the use and effect of regulatory pathways intended to address weak economic incentives in the face of urgent, unmet needs; evaluate the extent of data underpinning authorizations for these indications; document development timelines and evidence available at the time of each approval; consider explanations for observed differences; and discuss the implications for clinical guidelines and use.

Methods and findings

For each indication, we selected two drugs: one recently approved and one approved between 2012 and 2014, when the first new anti-TB drug from a novel class in more than 40 years received marketing authorization. We calculated time from first published peer-reviewed evidence of activity to date of approval; the number of phase 1, 2, and 3 trials; the number of trial participants randomized to treatment arms containing the drug; and the total number of participants in each trial from the individual drug approval packages. We found that the two TB drugs took longer to gain approval (8.0 and 19.2 years for bedaquiline and pretomanid, respectively) despite availing of special regulatory pathways meant to expedite approval, when compared to the HIV (2.6 years for dolutegravir and 4.7 years for doravirine) and HCV drugs (3.2 and 1.6 years for sofosbuvir and glecaprevir/pibrentasvir, respectively). Moreover, fewer participants were studied prior to TB drug approvals (380 and 879) than prior to approvals for HIV (1598 and 979) and for HCV (2291 and 2448) drugs.

Conclusions

The dramatic disparities observed in TB drug development reaffirm the importance of several actions. Increased investment in TB research and development is necessary to rapidly advance drugs through the pipeline. Development plans and partnerships must provide safety and efficacy evidence on combinations and durations that are relevant to real-world use in heterogeneous populations. Reliable, validated surrogate markers of relapse-free cure are essential to decrease the duration and cost of TB treatment trials and increase the confidence and speed with which new regimens can advance. Lastly, regulators and normative bodies must maintain high evidentiary standards for authorization while ensuring timely and broad approval for TB drugs and regimens.

Making cough count in tuberculosis care

Cough assessment is central to the clinical management of respiratory diseases, including tuberculosis (TB), but strategies to objectively and unobtrusively measure cough are lacking. Acoustic epidemiology is an emerging field that uses technology to detect cough sounds and analyze cough patterns to improve health outcomes among people with respiratory conditions linked to cough. This field is increasingly exploring the potential of artificial intelligence (AI) for more advanced applications, such as analyzing cough sounds as a biomarker for disease screening. While much of the data are preliminary, objective cough assessment could potentially transform disease control programs, including TB, and support individual patient management. Here, we present an overview of recent advances in this field and describe how cough assessment, if validated, could support public health programs at various stages of the TB care cascade.

Zimmer et al. discuss the importance of cough assessment in clinical management of tuberculosis (TB). They describe how acoustic epidemiology, which uses recording devices and artificial intelligence to detect, record and analyze cough, can be used in TB control and individual patient management.

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.

SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples

SynergyFinder (https://synergyfinder.fimm.fi) is a free web-application for interactive analysis and visualization of multi-drug combination response data. Since its first release in 2017, SynergyFinder has become a popular tool for multi-dose combination data analytics, partly because the development of its functionality and graphical interface has been driven by a diverse user community, including both chemical biologists and computational scientists. Here, we describe the latest upgrade of this community-effort, SynergyFinder release 3.0, introducing a number of novel features that support interactive multi-sample analysis of combination synergy, a novel consensus synergy score that combines multiple synergy scoring models, and an improved outlier detection functionality that eliminates false positive results, along with many other post-analysis options such as weighting of synergy by drug concentrations and distinguishing between different modes of synergy (potency and efficacy). Based on user requests, several additional improvements were also implemented, including new data visualizations and export options for multi-drug combinations. With these improvements, SynergyFinder 3.0 supports robust identification of consistent combinatorial synergies for multi-drug combinatorial discovery and clinical translation.

vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding

Background

As antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allow non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria.

Results

In this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis bacteria using our vCOMBAT tool.

Conclusions

The vCOMBAT online tool is publicly available at https://combat-bacteria.org/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04536-3.

Pharmacodynamics and the Bactericidal Activity of Bedaquiline in Pulmonary Tuberculosis

Bedaquiline is a diarylquinoline antimycobacterial drug and a key component of several regimens in clinical development for treatment of tuberculosis (TB), but with ongoing phase 3 trials that include assessment of simplified dosing. A pharmacokinetic-pharmacodynamic model of bedaquiline Mycobacterium tuberculosis killing kinetics in adults with pulmonary TB was developed to inform dose selection of bedaquiline-containing regimens. The model parameters were estimated with data from the 14-day early bactericidal activity (EBA) study TMC207-CL001 conducted in Cape Town, South Africa. The study included 60 adult males and females with drug-susceptible pulmonary TB, who were administered bedaquiline with loading doses on the first two days followed by once daily 100 mg, 200 mg, 300 mg, or 400 mg. The modeling results included expected values (mean±SD) for a maximum drug kill rate constant equal to 0.23±0.03 log₁₀ CFU/mL sputum/day, a half-maximum effect plasma concentration equal to 1.6±0.3 mg/L, and an average time to onset of activity equal to 40±7 h. Model simulations showed once daily 200 mg, 300 mg, and 400 mg (without loading doses) attained 40%, 50%, and 60%, respectively, of an expected maximum 14-day EBA equal to 0.18 log₁₀ CFU/mL/day, or 10 h/day assessed by liquid culture time to positivity (TTP). Additional simulations illustrated efficacy outcomes during eight weeks of treatment with the recommended and alternative dosages. The results demonstrate a general mathematical and statistical approach to analysis of EBA studies with broad application to TB regimen development.

The pipeline of new molecules and regimens against drug-resistant tuberculosis

The clinical development and regulatory approval of bedaquiline, delamanid and pretomanid over the last decade brought about significant progress in the management of drug-resistant tuberculosis, providing all-oral regimens with favorable safety profiles. The Nix-TB and ZeNix trials of a bedaquiline - pretomanid - linezolid regimen demonstrated for the first time that certain forms of drug-resistant tuberculosis can be cured in the majority of patients within 6 months. Ongoing Phase 3 studies containing these drugs may further advance optimized regimen compositions. Investigational drugs in clinical development that target clinically validated mechanisms, such as second generation oxazolidinones (sutezolid, delpazolid, TBI-223) and diarylquinolines (TBAJ-876 and TBAJ-587) promise improved potency and/or safety compared to the first-in-class drugs. Compounds with novel targets involved in diverse bacterial functions such as cell wall synthesis (DrpE1, MmpL3), electron transport, DNA synthesis (GyrB), cholesterol metabolism and transcriptional regulation of ethionamide bioactivation pathways have advanced to early clinical studies with the potential to enhance antibacterial activity when added to new or established anti-TB drug regimens. Clinical validation of preclinical in vitro and animal model predictions of new anti-TB regimens may further improve the translational value of these models to identify optimal anti-TB therapies.

Pharmacokinetics and Target Attainment of SQ109 in Plasma and Human-Like Tuberculosis Lesions in Rabbits

SQ109 is a novel well-tolerated drug candidate in clinical development for the treatment of drug-resistant tuberculosis (TB). It is the only inhibitor of the MmpL3 mycolic acid transporter in clinical development. No SQ109-resistant mutant has been directly isolated thus far in vitro, in mice, or in patients, which is tentatively attributed to its multiple targets. It is considered a potential replacement for poorly tolerated components of multidrug-resistant TB regimens. To prioritize SQ109-containing combinations with the best potential for cure and treatment shortening, one must understand its contribution against different bacterial populations in pulmonary lesions. Here, we have characterized the pharmacokinetics of SQ109 in the rabbit model of active TB and its penetration at the sites of disease—lung tissue, cellular and necrotic lesions, and caseum. A two-compartment model with first-order absorption and elimination described the plasma pharmacokinetics. At the human-equivalent dose, parameter estimates fell within the ranges published for preclinical species. Tissue concentrations were modeled using an “effect” compartment, showing high accumulation in lung and cellular lesion areas with penetration coefficients in excess of 1,000 and lower passive diffusion in caseum after 7 daily doses. These results, together with the hydrophobic nature and high nonspecific caseum binding of SQ109, suggest that multiweek dosing would be required to reach steady state in caseum and poorly vascularized compartments, similar to bedaquiline. Linking lesion pharmacokinetics to SQ109 potency in assays against replicating, nonreplicating, and intracellular M. tuberculosis showed SQ109 concentrations markedly above pharmacokinetic-pharmacodynamic targets in lung and cellular lesions throughout the dosing interval.

Pretomanid dose selection for pulmonary tuberculosis: An application of multi-objective optimization to dosage regimen design

Clinical development of combination chemotherapies for tuberculosis (TB) is complicated by partial or restricted phase II dose‐finding. Barriers include a propensity for drug resistance with monotherapy, practical limits on numbers of treatment arms for component dose combinations, and limited application of current dose selection methods to multidrug regimens. A multi‐objective optimization approach to dose selection was developed as a conceptual and computational framework for currently evolving approaches to clinical testing of novel TB regimens. Pharmacokinetic‐pharmacodynamic (PK‐PD) modeling was combined with an evolutionary algorithm to identify dosage regimens that yield optimal trade‐offs between multiple conflicting therapeutic objectives. The phase IIa studies for pretomanid, a newly approved nitroimidazole for specific cases of highly drug‐resistant pulmonary TB, were used to demonstrate the approach with Pareto optimized dosing that best minimized sputum bacillary load and the probability of drug‐related adverse events. Results include a population‐typical characterization of the recommended 200 mg once daily dosage, the optimality of time‐dependent dosing, examples of individualized therapy, and the determination of optimal loading doses. The approach generalizes conventional PK‐PD target attainment to a design problem that scales to drug combinations, and provides a benefit‐risk context for clinical testing of complex drug regimens.

Fourteen-day PET/CT imaging to monitor drug combination activity in treated individuals with tuberculosis

Early bactericidal activity studies monitor daily sputum bacterial counts in individuals with tuberculosis (TB) for 14 days during experimental drug treatment. The rate of change in sputum bacterial load over time provides an informative, but imperfect, estimate of drug activity and is considered a critical step in development of new TB drugs. In this clinical study, 160 participants with TB received isoniazid, pyrazinamide, or rifampicin, components of first-line chemotherapy, and moxifloxacin individually and in combination. In addition to standard bacterial enumeration in sputum, participants underwent 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography and computerized tomography ([18F]FDG-PET/CT) at the beginning and end of the 14-day drug treatment. Quantitating radiological responses to drug treatment provided comparative single and combination drug activity measures across lung lesion types that correlated more closely with established clinical outcomes when combined with sputum enumeration compared to sputum enumeration alone. Rifampicin and rifampicin-containing drug combinations were most effective in reducing both lung lesion volume measured by CT imaging and lesion-associated inflammation measured by PET imaging. Moxifloxacin was not superior to rifampicin in any measure by PET/CT imaging, consistent with its performance in recent phase 3 clinical trials. PET/CT imaging revealed synergy between isoniazid and pyrazinamide and demonstrated that the activity of pyrazinamide was limited to lung lesion, showing the highest FDG uptake during the first 2 weeks of drug treatment. [18F]FDG-PET/CT imaging may be useful for measuring the activity of single drugs and drug combinations during evaluation of potential new TB drug regimens before phase 3 trials.

All nonadherence is equal but is some more equal than others? Tuberculosis in the digital era

Adherence to treatment for tuberculosis (TB) has been a concern for many decades, resulting in the World Health Organization's recommendation of the direct observation of treatment in the 1990s. Recent advances in digital adherence technologies (DATs) have renewed discussion on how to best address nonadherence, as well as offering important information on dose-by-dose adherence patterns and their variability between countries and settings. Previous studies have largely focussed on percentage thresholds to delineate sufficient adherence, but this is misleading and limited, given the complex and dynamic nature of adherence over the treatment course. Instead, we apply a standardised taxonomy - as adopted by the international adherence community - to dose-by-dose medication-taking data, which divides missed doses into 1) late/noninitiation (starting treatment later than expected/not starting), 2) discontinuation (ending treatment early), and 3) suboptimal implementation (intermittent missed doses). Using this taxonomy, we can consider the implications of different forms of nonadherence for intervention and regimen design. For example, can treatment regimens be adapted to increase the "forgiveness" of common patterns of suboptimal implementation to protect against treatment failure and the development of drug resistance? Is it reasonable to treat all missed doses of treatment as equally problematic and equally common when deploying DATs? Can DAT data be used to indicate the patients that need enhanced levels of support during their treatment course? Critically, we pinpoint key areas where knowledge regarding treatment adherence is sparse and impeding scientific progress.

SynergyFinder 2.0: visual analytics of multi-drug combination synergies

SynergyFinder (https://synergyfinder.fimm.fi) is a stand-alone web-application for interactive analysis and visualization of drug combination screening data. Since its first release in 2017, SynergyFinder has become a widely used web-tool both for the discovery of novel synergistic drug combinations in pre-clinical model systems (e.g. cell lines or primary patient-derived cells), and for better understanding of mechanisms of combination treatment efficacy or resistance. Here, we describe the latest version of SynergyFinder (release 2.0), which has extensively been upgraded through the addition of novel features supporting especially higher-order combination data analytics and exploratory visualization of multi-drug synergy patterns, along with automated outlier detection procedure, extended curve-fitting functionality and statistical analysis of replicate measurements. A number of additional improvements were also implemented based on the user requests, including new visualization and export options, updated user interface, as well as enhanced stability and performance of the web-tool. With these improvements, SynergyFinder 2.0 is expected to greatly extend its potential applications in various areas of multi-drug combinatorial screening and precision medicine.

Early Bactericidal Activity of Different Isoniazid Doses for Drug-Resistant Tuberculosis (INHindsight): A Randomized, Open-Label Clinical Trial

Rationale: High-dose isoniazid is recommended in short-course regimens for multidrug-resistant tuberculosis (TB). The optimal dose of isoniazid and its individual contribution to efficacy against TB strains with inhA or katG mutations are unknown.Objectives: To define the optimal dose of isoniazid for patients with isoniazid-resistant TB mediated by inhA mutations.Methods: AIDS Clinical Trials Group A5312 is a phase 2A, open-label trial in which individuals with smear-positive pulmonary TB with isoniazid resistance mediated by an inhA mutation were randomized to receive isoniazid 5, 10, or 15 mg/kg daily for 7 days (inhA group), and control subjects with drug-sensitive TB received the standard dose (5 mg/kg/d). Overnight sputum cultures were collected daily. The 7-day early bactericidal activity (EBA) of isoniazid was estimated as the average daily change in log10 cfu on solid media (EBAcfu0-7) or as time to positivity (TTP) in liquid media in hours (EBATTP0-7) using nonlinear mixed-effects models.Measurements and Main Results: Fifty-nine participants (88% with cavitary disease, 20% HIV-positive, 16 with isoniazid-sensitive TB, and 43 with isoniazid-monoresistant or multidrug-resistant TB) were enrolled at one site in South Africa. The mean EBAcfu0-7 at doses of 5, 10, and 15 mg/kg in the inhA group was 0.07, 0.17, and 0.22 log10 cfu/ml/d, respectively, and 0.16 log10 cfu/ml/d in control subjects. EBATTP0-7 patterns were similar. There were no drug-related grade ≥3 adverse events.Conclusions: Isoniazid 10-15 mg/kg daily had activity against TB strains with inhA mutations similar to that of 5 mg/kg against drug-sensitive strains. The activity of high-dose isoniazid against strains with katG mutations will be explored next.Clinical trial registered with www.clinicaltrials.gov (NCT01936831).

Advances in clinical trial design: Weaving tomorrow's TB treatments

Christian Lienhardt and co-authors discuss the conclusions of the PLOS Medicine Collection on advances in clinical trial design for development of new tuberculosis treatments.

Development of new TB regimens: Harmonizing trial design, product registration requirements, and public health guidance

Christian Lienhardt and colleagues discuss the importance of communication and coordination between regulators, researchers, and policy makers to ensure tuberculosis trials provide high-quality evidence for policy decisions.