Hollow-fibre system model of tuberculosis reproducibility and performance specifications for best practice in drug and combination therapy development.

BACKGROUND: The hollow-fibre system model of tuberculosis (HFS-TB) has been endorsed by regulators; however, application of HFS-TB requires a thorough understanding of intra- and inter-team variability, statistical power and quality controls. METHODS: Three teams evaluated regimens matching those in the Rapid Evaluation of Moxifloxacin in Tuberculosis (REMoxTB) study, plus two high-dose rifampicin/pyrazinamide/moxifloxacin regimens, administered daily for up to 28 or 56 days against Mycobacterium tuberculosis (Mtb) under log-phase growth, intracellular growth or semidormant growth under acidic conditions. Target inoculum and pharmacokinetic parameters were pre-specified, and the accuracy and bias at achieving these calculated using percent coefficient of variation (%CV) at each sampling point and two-way analysis of variance (ANOVA). RESULTS: A total of 10 530 individual drug concentrations, and 1026 individual cfu counts were measured. The accuracy in achieving intended inoculum was >98%, and >88% for pharmacokinetic exposures. The 95% CI for the bias crossed zero in all cases. ANOVA revealed that the team effect accounted for <1% of variation in log10 cfu/mL at each timepoint. The %CV in kill slopes for each regimen and different Mtb metabolic populations was 5.10% (95% CI: 3.36%-6.85%). All REMoxTB arms exhibited nearly identical kill slopes whereas high dose regimens were 33% faster. Sample size analysis revealed that at least three replicate HFS-TB units are needed to identify >20% difference in slope, with a power of >99%. CONCLUSIONS: HFS-TB is a highly tractable tool for choosing combination regimens with little variability between teams, and between replicates.

Recommendations to address key recruitment challenges of Alzheimer's disease clinical trials.

Clinical trials for Alzheimer's disease (AD) are slower to enroll study participants, take longer to complete, and are more expensive than trials in most other therapeutic areas. The recruitment and retention of a large number of qualified, diverse volunteers to participate in clinical research studies remain among the key barriers to the successful completion of AD clinical trials. An advisory panel of experts from academia, patient-advocacy organizations, philanthropy, non-profit, government, and industry convened in 2020 to assess the critical challenges facing recruitment in Alzheimer's clinical trials and develop a set of recommendations to overcome them. This paper briefly reviews existing challenges in AD clinical research and discusses the feasibility and implications of the panel's recommendations for actionable and inclusive solutions to accelerate the development of novel therapies for AD.

Sputum lipoarabinomannan (LAM) as a biomarker to determine sputum mycobacterial load: exploratory and model-based analyses of integrated data from four cohorts.

BACKGROUND: Despite the high global disease burden of tuberculosis (TB), the disease caused by Mycobacterium tuberculosis (Mtb) infection, novel treatments remain an urgent medical need. Development efforts continue to be hampered by the reliance on culture-based methods, which often take weeks to obtain due to the slow growth rate of Mtb. The availability of a "real-time" measure of treatment efficacy could accelerate TB drug development. Sputum lipoarabinomannan (LAM; an Mtb cell wall glycolipid) has promise as a pharmacodynamic biomarker of mycobacterial sputum load. METHODS: The present analysis evaluates LAM as a surrogate for Mtb burden in the sputum samples from 4 cohorts of a total of 776 participants. These include those from 2 cohorts of 558 non-TB and TB participants prior to the initiation of treatment (558 sputum samples), 1 cohort of 178 TB patients under a 14-day bactericidal activity trial with various mono- or multi-TB drug therapies, and 1 cohort of 40 TB patients with data from the first 56-day treatment of a standard 4-drug regimen. RESULTS: Regression analysis demonstrated that LAM was a predictor of colony-forming unit (CFU)/mL values obtained from the 14-day treatment cohort, with well-estimated model parameters (relative standard error ≤ 22.2%). Moreover, no changes in the relationship between LAM and CFU/mL were observed across the different treatments, suggesting that sputum LAM can be used to reasonably estimate the CFU/mL in the presence of treatment. The integrated analysis showed that sputum LAM also appears to be as good a predictor of time to Mycobacteria Growth Incubator Tube (MGIT) positivity as CFU/mL. As a binary readout, sputum LAM positivity is a strong predictor of solid media or MGIT culture positivity with an area-under-the-curve value of 0.979 and 0.976, respectively, from receiver-operator curve analysis. CONCLUSIONS: Our results indicate that sputum LAM performs as a pharmacodynamic biomarker for rapid measurement of Mtb burden in sputum, and thereby may enable more efficient early phase clinical trial designs (e.g., adaptive designs) to compare candidate anti-TB regimens and streamline dose selection for use in pivotal trials. Trial registration NexGen EBA study (NCT02371681).

Duration of pretomanid/moxifloxacin/pyrazinamide therapy compared with standard therapy based on time-to-extinction mathematics.

OBJECTIVES: Animal models have suggested that the combination of pretomanid with pyrazinamide and moxifloxacin (PaMZ) may shorten TB therapy duration to 3-4 months. Here, we tested that in the hollow-fibre system model of TB (HFS-TB). METHODS: A series of HFS-TB experiments were performed to compare the kill rates of the PaMZ regimen with the standard three-drug combination therapy. HFS-TB experiments were performed with bacilli in log-phase growth treated for 28 days, intracellular bacilli treated daily for 28 days and semi-dormant Mycobacterium tuberculosis treated with daily therapy for 56 days for sterilizing effect. Next, time-to-extinction equations were employed, followed by morphism transformation and Latin hypercube sampling, to determine the proportion of patients who achieved a time to extinction of 3, 4 or 6 months with each regimen. RESULTS: Using linear regression, the HFS-TB sterilizing effect rates of the PaMZ regimen versus the standard-therapy regimen during the 56 days were 0.18 (95% credible interval=0.13-0.23) versus 0.15 (95% credible interval=0.08-0.21) log10 cfu/mL/day, compared with 0.16 (95% credible interval=0.13-0.18) versus 0.11 (95% credible interval=0.09-0.13) log10 cfu/mL/day in the Phase II clinical trial, respectively. Using time-to-extinction and Latin hypercube sampling modelling, the expected percentages of patients in which the PaMZ regimen would achieve sterilization were 40.37% (95% credible interval=39.1-41.34) and 72.30% (95% credible interval=71.41-73.17) at 3 and 4 months duration of therapy, respectively, versus 93.67% (95% credible interval=93.18-94.13) at 6 months for standard therapy. CONCLUSIONS: The kill rates of the PaMZ regimen were predicted to be insufficient to achieve cure in less than 6 months in most patients.

The importance of drug safety and tolerability in the development of new immunosuppressive therapy for transplant recipients: The Transplant Therapeutics Consortium's position statement.

The Transplant Therapeutics Consortium (TTC) is a public-private partnership between the US Food and Drug Administration and the transplantation community including the transplantation societies and members of the biopharmaceutical industry. The TTC was formed to accelerate the process of developing new medical products for transplant patients. The initial goals of this collaboration are the following: (a) To define which aspects of the kidney transplant drug-development process have clear needs for improvement from an industry and regulatory perspective; (b) to define which of the unmet needs in the process could be positively impacted through the development of specific drug-development tools based on available data; and (c) to determine the most appropriate pathway to achieve regulatory acceptance of the proposed process-accelerating tools. The TTC has identified 2 major areas of emphasis: new biomarkers or endpoints for determining the efficacy of new therapies and new tools to assess the safety or tolerability of new therapies. This article presents the rationale and planned approach to develop new tools to assess safety and tolerability of therapies for transplant patients. We also discuss how similar efforts might support the continued development of patient-reported outcome measures in the future.

Recommendations for the design of therapeutic trials for neonatal seizures.

Although seizures have a higher incidence in neonates than any other age group and are associated with significant mortality and neurodevelopmental disability, treatment is largely guided by physician preference and tradition, due to a lack of data from well-designed clinical trials. There is increasing interest in conducting trials of novel drugs to treat neonatal seizures, but the unique characteristics of this disorder and patient population require special consideration with regard to trial design. The Critical Path Institute formed a global working group of experts and key stakeholders from academia, the pharmaceutical industry, regulatory agencies, neonatal nurse associations, and patient advocacy groups to develop consensus recommendations for design of clinical trials to treat neonatal seizures. The broad expertise and perspectives of this group were invaluable in developing recommendations addressing: (1) use of neonate-specific adaptive trial designs, (2) inclusion/exclusion criteria, (3) stratification and randomization, (4) statistical analysis, (5) safety monitoring, and (6) definitions of important outcomes. The guidelines are based on available literature and expert consensus, pharmacokinetic analyses, ethical considerations, and parental concerns. These recommendations will ultimately facilitate development of a Master Protocol and design of efficient and successful drug trials to improve the treatment and outcome for this highly vulnerable population.

Towards regulatory endorsement of drug development tools to promote the application of model-informed drug development in Duchenne muscular dystrophy.

Drug development for rare diseases is challenged by small populations and limited data. This makes development of clinical trial protocols difficult and contributes to the uncertainty around whether or not a potential therapy is efficacious. The use of data standards to aggregate data from multiple sources, and the use of such integrated databases to develop statistical models can inform protocol development and reduce the risks in developing new therapies. Achieving regulatory endorsement of such models through defined pathways at the US Food and Drug Administration and European Medicines Authority allows such tools to be used by the drug development community for defined contexts of use without further need for discussion of the underlying model(s). The Duchenne Regulatory Science Consortium (D-RSC) has brought together multiple stakeholders to develop a clinical trial simulation tool for Duchenne muscular dystrophy using such an approach. Here we describe the work of D-RSC as an example of how such an approach may be effective at reducing uncertainty in drug development for rare diseases, and thus bringing effective therapies to patients faster.

Quantitative approach for cardiac risk assessment and interpretation in tuberculosis drug development.

Cardiotoxicity is among the top drug safety concerns, and is of specific interest in tuberculosis, where this is a known or potential adverse event of current and emerging treatment regimens. As there is a need for a tool, beyond the QT interval, to quantify cardiotoxicity early in drug development, an empirical decision tree based classifier was developed to predict the risk of Torsades de pointes (TdP). The cardiac risk algorithm was developed using pseudo-electrocardiogram (ECG) outputs derived from cardiac myocyte electromechanical model simulations of increasing concentrations of 96 reference compounds which represented a range of clinical TdP risk. The algorithm correctly classified 89% of reference compounds with moderate sensitivity and high specificity (71 and 96%, respectively) as well as 10 out of 12 external validation compounds and the anti-TB drugs moxifloxacin and bedaquiline. The cardiac risk algorithm is suitable to help inform early drug development decisions in TB and will evolve with the addition of emerging data.

Toward an Evidence-Based Nonclinical Road Map for Evaluating the Efficacy of New Tuberculosis (TB) Drug Regimens: Proceedings of a Critical Path to TB Drug Regimens-National Institute of Allergy and Infectious Diseases In Vivo Pharmacology Workshop for TB Drug Development.

Novel tuberculosis (TB) drug regimens are urgently needed, and their development will be enabled by improved preclinical approaches that more effectively inform and ensure safe selection of clinical candidates and drug combination/regimens. An evidence-based approach for the assessment of nonclinical models supporting TB drug development has been proposed by a joint partnership between the National Institute of Allergy and Infectious Diseases (NIAID) and the Critical Path to TB Drug Regimens (CPTR) Consortium.

Big data to smart data in Alzheimer's disease: Real-world examples of advanced modeling and simulation.

Many disease-modifying clinical development programs in Alzheimer's disease (AD) have failed to date, and development of new and advanced preclinical models that generate actionable knowledge is desperately needed. This review reports on computer-based modeling and simulation approach as a powerful tool in AD research. Statistical data-analysis techniques can identify associations between certain data and phenotypes, such as diagnosis or disease progression. Other approaches integrate domain expertise in a formalized mathematical way to understand how specific components of pathology integrate into complex brain networks. Private-public partnerships focused on data sharing, causal inference and pathway-based analysis, crowdsourcing, and mechanism-based quantitative systems modeling represent successful real-world modeling examples with substantial impact on CNS diseases. Similar to other disease indications, successful real-world examples of advanced simulation can generate actionable support of drug discovery and development in AD, illustrating the value that can be generated for different stakeholders.

Big data to smart data in Alzheimer's disease: The brain health modeling initiative to foster actionable knowledge.

Massive investment and technological advances in the collection of extensive and longitudinal information on thousands of Alzheimer patients results in large amounts of data. These "big-data" databases can potentially advance CNS research and drug development. However, although necessary, they are not sufficient, and we posit that they must be matched with analytical methods that go beyond retrospective data-driven associations with various clinical phenotypes. Although these empirically derived associations can generate novel and useful hypotheses, they need to be organically integrated in a quantitative understanding of the pathology that can be actionable for drug discovery and development. We argue that mechanism-based modeling and simulation approaches, where existing domain knowledge is formally integrated using complexity science and quantitative systems pharmacology can be combined with data-driven analytics to generate predictive actionable knowledge for drug discovery programs, target validation, and optimization of clinical development.

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.

Strategic Regulatory Evaluation and Endorsement of the Hollow Fiber Tuberculosis System as a Novel Drug Development Tool.

The first nonclinical drug development tool (DDT) advanced by the Critical Path to TB Drug Regimens (CPTR) Initiative through a regulatory review process has been endorsed by leading global regulatory authorities. DDTs with demonstrated predictive accuracy for clinical and microbiological outcomes are needed to support decision making. Regulatory endorsement of these DDTs is critical for drug developers, as it promotes confidence in their use in Investigational New Drug and New Drug Application filings. The in vitro hollow fiber system model of tuberculosis (HFS-TB) is able to recapitulate concentration-time profiles (exposure) observed in patients for single drugs and combinations, by evaluating exposure measures for the ability to kill tuberculosis in different physiologic conditions. Monte Carlo simulations make this quantitative output useful to inform susceptibility breakpoints, dosage, and optimal combination regimens in patients, and to design nonclinical experiments in animal models. The Pre-Clinical and Clinical Sciences Working Group within CPTR executed an evidence-based evaluation of the HFS-TB for predictive accuracy. This extensive effort was enabled through the collaboration of subject matter experts representing the pharmaceutical industry, academia, product development partnerships, and regulatory authorities including the Food and Drug Administration (FDA) and the European Medicines Agency (EMA). A comprehensive analysis plan following the regulatory guidance documents for DDT qualification was developed, followed by individual discussions with the FDA and the EMA. The results from the quantitative analyses were submitted to both agencies, pursuing regulatory DDT endorsement. The EMA Qualification Opinion for the HFS-TB DDT was published 26 January 2015 (available at: http://www.ema.europa.eu/ema/index.jsp?curl=pages/regulation/document_listing/document_listing_000319.jsp).

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.

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.

Forecasting Accuracy of the Hollow Fiber Model of Tuberculosis for Clinical Therapeutic Outcomes.

BACKGROUND: The hollow fiber system model of tuberculosis (HFS-TB), in tandem with Monte Carlo experiments, represents a drug development tool (DDT) with the potential for use to develop tuberculosis treatment regimens. However, the predictive accuracy of the HFS-TB, or any other nonclinical DDT such as an animal model, has yet to be robustly evaluated. METHODS: To avoid hindsight bias, a literature search was performed to identify clinical studies published at least 6 months after HFS-TB experiments' quantitative predictions. Steps to minimize bias and for reporting systematic reviews were applied as outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Publications were scored for quality of evidence. Accuracy was calculated using the mean absolute percentage error, then summated with weighting assigned by sample size and quality-of-evidence score. Given the lack of a gold-standard tuberculosis DDT, the forecasting accuracy of a completely unreliable tool was also calculated from 1000 simulated experiments for a random or "total guesswork" model. RESULTS: The quantitative forecasting accuracy (95% confidence interval [CI]) for the "total guesswork" model was 15.6% (95% CI, 8.7%-22.5%); bias was -0.1% (95% CI, -2.5% to 2.2%). Twenty clinical studies were published after HFS-TB experiments predicted optimal drug exposures and doses, susceptibility breakpoints, and optimal combination regimens. Based on these clinical studies, the predictive accuracy of the HFS-TB was 94.4% (95% CI, 84.3%-99.9%), and bias was 1.8% (95% CI, -13.7% to 6.2%). CONCLUSIONS: The HFS-TB model is highly accurate at forecasting optimal drug exposures, doses, and dosing schedules for use in the clinic.

Development of a unified clinical trial database for Alzheimer's disease.

INTRODUCTION: Data obtained in completed Alzheimer's disease (AD) clinical trials can inform decision making for future trials. Recognizing the importance of sharing these data, the Coalition Against Major Diseases created an Online Data Repository for AD (CODR-AD) with the aim of supporting accelerated drug development. The aim of this study was to build an open access, standardized database from control arm data collected across many clinical trials. METHODS: Comprehensive AD-specific data standards were developed to enable the pooling of data from different sources. Nine member organizations contributed patient-level data from 24 clinical trials of AD treatments. RESULTS: CODR-AD consists of control arm pooled and standardized data from 24 trials currently numbered at 6500 subjects; Alzheimer's Disease Assessment Scale-cognitive subscale 11 is the main outcome and specific covariates are also included. DISCUSSION: CODR-AD represents a unique integrated standardized clinical trials database available to qualified researchers. The pooling of data across studies facilitates a more comprehensive understanding of disease heterogeneity.

Elements for adequate informed consent in the surgical context.

Given a history of atrocities and violations of ethical principles, several documents and regulations have been issued by a wide variety of organizations. They aim at ensuring that health care and clinical research adhere to defined ethical principles. A fundamental component was devised to ensure that the individual has been provided the necessary information to make an informed decision regarding health care or participation in clinical research. This article summarizes the history and regulations for informed consent and discusses suggested components for adequate consent forms for daily clinical practice in surgery as well as clinical research.

Association of patient demographics on quality of life in a sample of adult patients with cardiac arrhythmias.

PURPOSE: Identify predictors of quality of life (QOL) in patients with any form of cardiac arrhythmia (CA). METHODS: Data from the Medical Panel Expenditure Survey were analyzed from 2004 to 2009. Patients aged ≥18 with any form of CA (identified via ICD-9-CM codes) were included. Primary outcomes included the physical and mental component scores (PCS and MCS) of the Short-Form 12 version 2 (SF-12) and EuroQoL-5D (EQ-5D) utility scores (US version). Patient demographics included insurance status, urban status, geographical region, federal poverty level, education, comorbidities, and disease-related risk factors of CA. RESULTS: Approximately 5,750,440 individuals had CA. Non-Hispanic Whites had the highest SF-12 MCS (mean 50.9; p < 0.001 across racial groups) and utility scores (mean 0.76; p < 0.001 across racial groups). Patients with both private and public insurance had significantly higher PCS (p = 0.001) and MCS (p < 0.001) in comparison with patients only covered by public insurance. Patients on antiarrhythmic agents had higher SF-12 MCS (51.4 vs. 48.4; p < 0.001) compared to individuals not on antiarrhythmic agents. CONCLUSIONS: Significantly lower QOL existed in specific subpopulations (e.g., patients with only public health insurance, racial/ethnic minorities, and those not exposed to antiarrhythmic agents) within the CA population.

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.