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Simeon S, Garcia-Cremades M, Savic R, Solans BP. Pharmacokinetic-pharmacodynamic modeling of tuberculosis time to positivity and colony-forming unit to assess the response to dose-ranging linezolid. Antimicrob Agents Chemother 2024; 68:e0019024. [PMID: 39016594 PMCID: PMC11323931 DOI: 10.1128/aac.00190-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 06/08/2024] [Indexed: 07/18/2024] Open
Abstract
According to the World Health Organization, the number of tuberculosis (TB) infections and the drug-resistant burden worldwide increased by 4.5% and 3.0%, respectively, between 2020 and 2021. Disease severity and complexity drive the interest for undertaking new clinical trials to provide efficient treatment to limit spread and drug resistance. TB Alliance conducted a phase 2 study in 106 patients to guide linezolid (LZD) dose selection using early bactericidal activity over 14 days of treatment. LZD is highly efficient for drug-resistant TB treatment, but treatment monitoring is required since serious adverse events can occur. The objective of this study was to develop a pharmacokinetic-pharmacodynamic (PKPD) model to analyze the dose-response relationship between linezolid exposure and efficacy biomarkers. Using time to positivity (TTP) and colony-forming unit (CFU) count data, we developed a PKPD model in six dosing regimens, differing on LZD dosing intensity. A one-compartment model with five transit absorption compartments and non-linear auto-inhibition elimination described best LZD pharmacokinetic characteristics. TTP and CFU logarithmic scaled [log(CFU)] showed a bactericidal activity of LZD against Mycobacterium tuberculosis. TTP was defined by a model with two significant covariates: the presence of uni- and bilateral cavities decreased baseline TTP value by 24%, and an increase on every 500 mg/L/h of cumulative area under the curve increased the rate at which TTP and CFU change from baseline by 20% and 11%, respectively. CLINICAL TRIALS This study is registered with ClinicalTrials.gov as NCT02279875.
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Affiliation(s)
- Segolene Simeon
- Department of
Bioengineering and Therapeutic Sciences, University of California San
Francisco Schools of Pharmacy and
Medicine, San Francisco,
California, USA
- UCSF Center for
Tuberculosis, University of California,
San Francisco, California,
USA
| | - Maria Garcia-Cremades
- Department of
Bioengineering and Therapeutic Sciences, University of California San
Francisco Schools of Pharmacy and
Medicine, San Francisco,
California, USA
- Department of
Pharmaceutics and Food Technology, School of Pharmacy, Complutense
University of Madrid,
Madrid, Spain
- Institute of
Industrial Pharmacy, Complutense University of
Madrid, Madrid,
Spain
| | - Rada Savic
- Department of
Bioengineering and Therapeutic Sciences, University of California San
Francisco Schools of Pharmacy and
Medicine, San Francisco,
California, USA
- UCSF Center for
Tuberculosis, University of California,
San Francisco, California,
USA
| | - Belén P. Solans
- Department of
Bioengineering and Therapeutic Sciences, University of California San
Francisco Schools of Pharmacy and
Medicine, San Francisco,
California, USA
- UCSF Center for
Tuberculosis, University of California,
San Francisco, California,
USA
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Dufault SM, Crook AM, Rolfe K, Phillips PPJ. A flexible multi-metric Bayesian framework for decision-making in Phase II multi-arm multi-stage studies. Stat Med 2024; 43:501-513. [PMID: 38038137 DOI: 10.1002/sim.9961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 09/19/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023]
Abstract
We propose a multi-metric flexible Bayesian framework to support efficient interim decision-making in multi-arm multi-stage phase II clinical trials. Multi-arm multi-stage phase II studies increase the efficiency of drug development, but early decisions regarding the futility or desirability of a given arm carry considerable risk since sample sizes are often low and follow-up periods may be short. Further, since intermediate outcomes based on biomarkers of treatment response are rarely perfect surrogates for the primary outcome and different trial stakeholders may have different levels of risk tolerance, a single hypothesis test is insufficient for comprehensively summarizing the state of the collected evidence. We present a Bayesian framework comprised of multiple metrics based on point estimates, uncertainty, and evidence towards desired thresholds (a Target Product Profile) for (1) ranking of arms and (2) comparison of each arm against an internal control. Using a large public-private partnership targeting novel TB arms as a motivating example, we find via simulation study that our multi-metric framework provides sufficient confidence for decision-making with sample sizes as low as 30 patients per arm, even when intermediate outcomes have only moderate correlation with the primary outcome. Our reframing of trial design and the decision-making procedure has been well-received by research partners and is a practical approach to more efficient assessment of novel therapeutics.
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Affiliation(s)
- Suzanne M Dufault
- Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
- UCSF Center for Tuberculosis, University of California, San Francisco, CA, USA
| | - Angela M Crook
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, London, UK
| | | | - Patrick P J Phillips
- UCSF Center for Tuberculosis, University of California, San Francisco, CA, USA
- Division of Pulmonary and Critical Care Medicine, University of California, San Francisco, San Francisco, CA, USA
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Alffenaar JWC, de Steenwinkel JEM, Diacon AH, Simonsson USH, Srivastava S, Wicha SG. Pharmacokinetics and pharmacodynamics of anti-tuberculosis drugs: An evaluation of in vitro, in vivo methodologies and human studies. Front Pharmacol 2022; 13:1063453. [PMID: 36569287 PMCID: PMC9780293 DOI: 10.3389/fphar.2022.1063453] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
There has been an increased interest in pharmacokinetics and pharmacodynamics (PKPD) of anti-tuberculosis drugs. A better understanding of the relationship between drug exposure, antimicrobial kill and acquired drug resistance is essential not only to optimize current treatment regimens but also to design appropriately dosed regimens with new anti-tuberculosis drugs. Although the interest in PKPD has resulted in an increased number of studies, the actual bench-to-bedside translation is somewhat limited. One of the reasons could be differences in methodologies and outcome assessments that makes it difficult to compare the studies. In this paper we summarize most relevant in vitro, in vivo, in silico and human PKPD studies performed to optimize the drug dose and regimens for treatment of tuberculosis. The in vitro assessment focuses on MIC determination, static time-kill kinetics, and dynamic hollow fibre infection models to investigate acquisition of resistance and killing of Mycobacterium tuberculosis populations in various metabolic states. The in vivo assessment focuses on the various animal models, routes of infection, PK at the site of infection, PD read-outs, biomarkers and differences in treatment outcome evaluation (relapse and death). For human PKPD we focus on early bactericidal activity studies and inclusion of PK and therapeutic drug monitoring in clinical trials. Modelling and simulation approaches that are used to evaluate and link the different data types will be discussed. We also describe the concept of different studies, study design, importance of uniform reporting including microbiological and clinical outcome assessments, and modelling approaches. We aim to encourage researchers to consider methods of assessing and reporting PKPD of anti-tuberculosis drugs when designing studies. This will improve appropriate comparison between studies and accelerate the progress in the field.
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Affiliation(s)
- Jan-Willem C. Alffenaar
- Sydney Institute for Infectious Diseases, The University of Sydney, Sydney, NSW, Australia,School of Pharmacy, The University of Sydney Faculty of Medicine and Health, Sydney, NSW, Australia,Westmead Hospital, Sydney, NSW, Australia,*Correspondence: Jan-Willem C. Alffenaar,
| | | | | | | | - Shashikant Srivastava
- Department of Pulmonary Immunology, University of Texas Health Science Center at Tyler, Tyler, TX, United States
| | - Sebastian G. Wicha
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Hamburg, Germany
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Pharmacometrics in tuberculosis: progress and opportunities. Int J Antimicrob Agents 2022; 60:106620. [PMID: 35724859 DOI: 10.1016/j.ijantimicag.2022.106620] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/23/2022] [Accepted: 06/12/2022] [Indexed: 11/22/2022]
Abstract
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.
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Pai MP, Crass RL. Translation of Pharmacodynamic Biomarkers of Antibiotic Efficacy in Specific Populations to Optimize Doses. Antibiotics (Basel) 2021; 10:antibiotics10111368. [PMID: 34827306 PMCID: PMC8614818 DOI: 10.3390/antibiotics10111368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/03/2021] [Accepted: 11/05/2021] [Indexed: 11/16/2022] Open
Abstract
Antibiotic efficacy determination in clinical trials often relies on non-inferiority designs because they afford smaller study sample sizes. These efficacy studies tend to exclude patients within specific populations or include too few patients to discern potential differences in their clinical outcomes. As a result, dosing guidance in patients with abnormal liver and kidney function, age across the lifespan, and other specific populations relies on drug exposure-matching. The underlying assumption for exposure-matching is that the disease course and the response to the antibiotic are similar in patients with and without the specific condition. While this may not be the case, clinical efficacy studies are underpowered to ensure this is true. The current paper provides an integrative review of the current approach to dose selection in specific populations. We review existing clinical trial endpoints that could be measured on a more continuous rather than a discrete scale to better inform exposure-response relationships. The inclusion of newer systemic biomarkers of efficacy can help overcome the current limitations. We use a modeling and simulation exercise to illustrate how an efficacy biomarker can inform dose selection better. Studies that inform response-matching rather than exposure-matching only are needed to improve dose selection in specific populations.
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Affiliation(s)
- Manjunath P. Pai
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Rm 2568, 428 Church St., Ann Arbor, MI 48109, USA
- Correspondence: ; Tel.: +1-734-647-0006
| | - Ryan L. Crass
- Ann Arbor Pharmacometrics Group, Ann Arbor, MI 48108, USA;
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