Debra Hanna

Correlations Between the Hollow Fiber Model of Tuberculosis and Therapeutic Events in Tuberculosis Patients: Learn and Confirm

BACKGROUND The hollow fiber system model of tuberculosis (HFS-TB) is designed to perform pharmacokinetics/pharmacodynamics (PK/PD) experiments, and hence the design of optimal doses and dose schedules for the treatment of tuberculosis. To determine if this model is useful for deriving PK/PD data relevant to clinical outcomes, we compared its quantitative output to that from clinical trials. METHODS We performed a PubMed search to identify clinical studies performed with antituberculosis therapy in which PK/PD data and/or parameters were documented or a dose-scheduling study design was employed. The search period was from January 1943 to December 2012. All clinical studies were published prior to HFS-TB experiments. Bias minimization was done according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Clinical publications were scored for quality of evidence, with 1 as the highest score (randomized controlled trials or meta-analyses of such studies), and 4 as the lowest score. RESULTS We identified 17 studies that examined the same parameters as in 8 HFS-TB studies. Fifteen of 17 studies had a quality-of-evidence score of 1. The sterilizing and bactericidal effect rates for isoniazid, rifampin, pyrazinamide, and ethambutol were the same in the HFS-TB as in patients. Time to emergence of resistance for monotherapy was the same as in patients. The PK/PD indices associated with efficacy were the same in HFS-TB as in patients for all drugs examined. CONCLUSIONS The HFS-TB model is highly accurate at identifying optimal drug exposures, doses, and dosing schedules for use in the clinic.

Nonclinical Models for Antituberculosis Drug Development: A Landscape Analysis

BACKGROUND Several nonclinical drug-development tools (DDTs) have been used for antituberculosis drug development over several decades. The role of the DDTs used for evaluating the efficacy of antituberculosis drug combinations and the gaps in the evidence base for which new tools or approaches are needed are as yet undefined. METHODS We performed a landscape analysis based on a comprehensive literature review to create evidence based guidelines. RESULTS There are 3 important questions that a DDT should answer with regard to antituberculosis drugs: What combination(s) of drugs will be most effective? What dose(s) and schedule(s) of each drug should be administered? and What duration(s) of treatment will be efficacious? Four DDTs were identified as having a track record to answer these questions: in vitro susceptibility tests, the hollow fiber system model of tuberculosis, mice, and guinea pigs. No single nonclinical in vitro or animal model recapitulates all aspects of human tuberculosis. Therefore, a combination of models is recommended for drug development. Gaps identified include the need for standardization of nonclinical model experiments, evaluation of animal models with pathology more similar to that in humans, and identification of experimental quantitative output in the DDTs that correlates with sterilizing effect in humans. CONCLUSIONS There is a need for formal quantitative analyses of how well DDTs forecast clinical outcomes.

Systematic Analysis of Hollow Fiber Model of Tuberculosis Experiments

BACKGROUND The in vitro hollow fiber system model of tuberculosis (HFS-TB), in tandem with Monte Carlo experiments, was introduced more than a decade ago. Since then, it has been used to perform a large number of tuberculosis pharmacokinetics/pharmacodynamics (PK/PD) studies that have not been subjected to systematic analysis. METHODS We performed a literature search to identify all HFS-TB experiments published between 1 January 2000 and 31 December 2012. There was no exclusion of articles by language. Bias minimization was according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Steps for reporting systematic reviews were followed. RESULTS There were 22 HFS-TB studies published, of which 12 were combination therapy studies and 10 were monotherapy studies. There were 4 stand-alone Monte Carlo experiments that utilized quantitative output from the HFS-TB. All experiments reported drug pharmacokinetics, which recapitulated those encountered in humans. HFS-TB studies included log-phase growth studies under ambient air, semidormant bacteria at pH 5.8, and nonreplicating persisters at low oxygen tension of ≤ 10 parts per billion. The studies identified antibiotic exposures associated with optimal kill of Mycobacterium tuberculosis and suppression of acquired drug resistance (ADR) and informed predictions about optimal clinical doses, expected performance of standard doses and regimens in patients, and expected rates of ADR, as well as a proposal of new susceptibility breakpoints. CONCLUSIONS The HFS-TB model offers the ability to perform PK/PD studies including humanlike drug exposures, to identify bactericidal and sterilizing effect rates, and to identify exposures associated with suppression of drug resistance. Because of the ability to perform repetitive sampling from the same unit over time, the HFS-TB vastly improves statistical power and facilitates the execution of time-to-event analyses and repeated event analyses, as well as dynamic system pharmacology mathematical models.

A standardised method for interpreting the association between mutations and phenotypic drug resistance in Mycobacterium tuberculosis

A clear understanding of the genetic basis of antibiotic resistance in Mycobacterium tuberculosis is required to accelerate the development of rapid drug susceptibility testing methods based on genetic sequence. Raw genotype–phenotype correlation data were extracted as part of a comprehensive systematic review to develop a standardised analytical approach for interpreting resistance associated mutations for rifampicin, isoniazid, ofloxacin/levofloxacin, moxifloxacin, amikacin, kanamycin, capreomycin, streptomycin, ethionamide/prothionamide and pyrazinamide. Mutation frequencies in resistant and susceptible isolates were calculated, together with novel statistical measures to classify mutations as high, moderate, minimal or indeterminate confidence for predicting resistance. We identified 286 confidence-graded mutations associated with resistance. Compared to phenotypic methods, sensitivity (95% CI) for rifampicin was 90.3% (89.6–90.9%), while for isoniazid it was 78.2% (77.4–79.0%) and their specificities were 96.3% (95.7–96.8%) and 94.4% (93.1–95.5%), respectively. For second-line drugs, sensitivity varied from 67.4% (64.1–70.6%) for capreomycin to 88.2% (85.1–90.9%) for moxifloxacin, with specificity ranging from 90.0% (87.1–92.5%) for moxifloxacin to 99.5% (99.0–99.8%) for amikacin. This study provides a standardised and comprehensive approach for the interpretation of mutations as predictors of M. tuberculosis drug-resistant phenotypes. These data have implications for the clinical interpretation of molecular diagnostics and next-generation sequencing as well as efficient individualised therapy for patients with drug-resistant tuberculosis. A comprehensive basis for interpreting mutations to predict antibiotic resistance in tuberculosis http://ow.ly/hhwJ30g9jCY

Collaborative Effort for a Centralized Worldwide Tuberculosis Relational Sequencing Data Platform

Continued progress in addressing challenges associated with detection and management of tuberculosis requires new diagnostic tools. These tools must be able to provide rapid and accurate information for detecting resistance to guide selection of the treatment regimen for each patient. To achieve this goal, globally representative genotypic, phenotypic, and clinical data are needed in a standardized and curated data platform. A global partnership of academic institutions, public health agencies, and nongovernmental organizations has been established to develop a tuberculosis relational sequencing data platform (ReSeqTB) that seeks to increase understanding of the genetic basis of resistance by correlating molecular data with results from drug susceptibility testing and, optimally, associated patient outcomes. These data will inform development of new diagnostics, facilitate clinical decision making, and improve surveillance for drug resistance. ReSeqTB offers an opportunity for collaboration to achieve improved patient outcomes and to advance efforts to prevent and control this devastating disease.

Building a Roadmap for Developing Combination Therapies for Alzheimer's Disease

Combination therapy has proven to be an effective strategy for treating many of the world’s most intractable diseases. A growing number of investigators in academia, industry, regulatory agencies, foundations and advocacy organizations are interested in pursuing a combination approach to treating Alzheimer’s disease. A meeting co-hosted by the Accelerate Cure/Treatments for Alzheimer’s Disease Coalition, the Critical Path Institute and the Alzheimer’s Association addressed challenges in designing clinical trials to test multiple treatments in combination and outlined a roadmap for making such trials a reality.

Integration of Published Information Into a Resistance-Associated Mutation Database for Mycobacterium tuberculosis

Tuberculosis remains a major global public health challenge. Although incidence is decreasing, the proportion of drug-resistant cases is increasing. Technical and operational complexities prevent Mycobacterium tuberculosis drug susceptibility phenotyping in the vast majority of new and retreatment cases. The advent of molecular technologies provides an opportunity to obtain results rapidly as compared to phenotypic culture. However, correlations between genetic mutations and resistance to multiple drugs have not been systematically evaluated. Molecular testing of M. tuberculosis sampled from a typical patient continues to provide a partial picture of drug resistance. A database of phenotypic and genotypic testing results, especially where prospectively collected, could document statistically significant associations and may reveal new, predictive molecular patterns. We examine the feasibility of integrating existing molecular and phenotypic drug susceptibility data to identify associations observed across multiple studies and demonstrate potential for well-integrated M. tuberculosis mutation data to reveal actionable findings.

Charting a path toward combination therapy for Alzheimer's disease

It is acknowledged that progress in combined therapeutic approaches for Alzheimer’s disease (AD) will require an unprecedented level of collaboration. At a meeting co-hosted by the Accelerate Cure/Treatments for Alzheimer’s Disease Coalition and the Critical Path Institute, investigators from industry, academia and regulatory agencies agreed on the need for combinatorial approaches to treating AD. The need for advancing multiple targets includes recognition for novel adaptive trial designs that incorporate existing and new biomarkers to evaluate drug effects independently and in combination. A combination trial now being planned may test drugs targeting different pathogenic pathways or multiple targets along a common pathway. Collaborations and consortia-based strategies are pivotal for success and a regulatory framework is recommended for success.

Translating the Tuberculosis Research Agenda: Much Accomplished, but Much More to Be Done

Despite the availability of effective diagnostics and curative treatment regimens for tuberculosis, millions of people die each year of this disease. The steady global increase in the number of tuberculosis cases caused by multidrug-resistant and extensively drug-resistant strains of Mycobacterium tuberculosis are of major concern, especially in light of the thin tuberculosis drug pipeline. New tuberculosis drugs are undergoing clinical evaluation, and renewed hope comes from fresh approaches to improve treatment outcomes using a range of adjunct host-directed cellular and repurposed drug therapies. Current efforts in developing second-generation and new rapid point-of-care diagnostic assays take advantage of recent genetic and molecular advances. Slow progress in the development of prophylactic and therapeutic vaccines requires increased funding for basic as well as translational research. Although major challenges remain, these can be overcome by cementing our resolve, raising advocacy, bolstering global funder investments, and leveraging more effective collaborations through equitable public-private partnerships.

Modeling and simulation for medical product development and evaluation: highlights from the FDA-C-Path-ISOP 2013 workshop

Medical-product development has become increasingly challenging and resource-intensive. In 2004, the Food and Drug Administration (FDA) described critical challenges facing medical-product development by establishing the critical path initiative [1]. Priorities identified included the need for improved modeling and simulation tools, further emphasized in FDA’s 2011 Strategic Plan for Regulatory Science [Appendix]. In an effort to support and advance model-informed medical-product development (MIMPD), the Critical Path Institute (C-Path) [www.c-path.org], FDA, and International Society of Pharmacometrics [www.go-isop.org] co-sponsored a workshop in Washington, D.C. on September 26, 2013, to examine integrated approaches to developing and applying model- MIMPD. The workshop brought together an international group of scientists from industry, academia, FDA, and the European Medicines Agency to discuss MIMPD strategies and their applications. A commentary on the proceedings of that workshop is presented here.