Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis

The value of model‐based translation in drug discovery and development is now effectively being recognized in many disease areas and among various stakeholders. Such quantitative approaches are expected to facilitate the selection on which compound to prioritize for successful development, predict the human efficacious dose based on preclinical data with adequate precision, guide design, and de‐risk later development stages. The importance of time‐dependencies, which are typically species‐dependent due to different turnover rates of biological processes, is, however, often neglected. For bacterial infections, the choice of dosing regimen is typically relying on preclinical pharmacokinetic (PK) and pharmacodynamic (PD) data, because the bacterial load and disease severity, and consequently the PK/PD relationship, cannot be quantified well on clinical data, given the low‐information end points used. It is time to recognize the limitations of using time‐collapsed approaches for translation (i.e., methods where targets are based on summary measures of exposure and response). Models describing the full time‐course captures important quantitative information of drug distribution, bacterial growth, antibiotic killing, and resistance development, and can account for species‐differences in the PK profiles driving the killing. Furthermore, with a model‐based approach for translation, we can take a holistic approach in development of a joint model for in vitro, in vivo, and clinical data, as well as incorporating information on the contribution of the immune system. Such advancements are anticipated to facilitate rational decision making during various stages of drug development and in the optimization of treatment regimens for different groups of patients.

Section: Translation In the New Eramentioning

confidence: 99%

“…In fact, the value of model-based translation for both antituberculosis and antimalarial treatments have recently been highlighted. 81,82 Of note, the threat of accelerating antiinfective drug resistance has not reduced during the SARS-CoV-2 pandemic.…”

Section: Translation In the New Eramentioning

confidence: 99%

Pivotal Role of Translation in Anti‐Infective Development

Friberg

2021

“…A clinical trial of rifapentine‐containing TB treatment‐shortening regimens (Study 31) investigated a 4‐month regimen that achieved noninferiority compared with the 6‐month standard‐of‐care regimen. MIDD played a pivotal role in the success of this trial 170–173 . First, a direct comparison in the chronic mouse model showed that the standard rifapentine dose (10 mg/kg) was superior to the standard rifampicin dose (10 mg/kg) in the mouse; however, this finding did not translate well to the earlier phase IIB study (Study 29 (https://clinicaltrials.gov/ct2/show/NCT00694629)), which evaluated rifapentine substitution for rifampin at this dose.…”

Section: Global Healthmentioning

confidence: 98%

“…MIDD played a pivotal role in the success of this trial. [170][171][172][173] First, a direct comparison in the chronic mouse model showed that the standard rifapentine dose (10 mg/kg) was superior to the standard rifampicin dose (10 mg/kg) in the mouse; however, this finding did not translate well to the earlier phase IIB study (Study 29 (https://clini caltr ials.gov/ct2/show/NCT00 694629)), which evaluated rifapentine substitution for rifampin at this dose. Therefore, the choice of an optimal rifapentine dose was based on clinical PK/PD work from a separate dose-ranging study.…”

Section: Tuberculosismentioning

confidence: 99%

“…These models have the potential to inform selection of the human‐equivalent dose and dosing schedule of a candidate drug used in a combination to then determine the likelihood of achieving treatment durations of 1 to 3 months with efficacy in both drug sensitive and drug resistant patients. Significant recent progress has been realized in the development of such systems pharmacology models (see below) to enable comparative efficacy evaluation and intended treatment‐shortening potential of novel regimens based on preclinical data and optimized translational simulations 166–170 . These model systems seek to integrate: (i) the quantification of the bacterial growth dynamics in the absence of treatment, (ii) the impact of immune system response in the absence and presence of treatment, (iii) the contribution of each drug (concentration–response relationship) to the observed total efficacy of drug combinations, and (iv) the interplay between disease pathology and drug response, including description of tissue penetration.…”

Section: Global Healthmentioning

confidence: 99%

See 1 more Smart Citation

Model‐Informed Drug Development for Anti‐Infectives: State of the Art and Future

Rayner

Smith

Andes

et al. 2021

Model‐informed drug development (MIDD) has a long and rich history in infectious diseases. This review describes foundational principles of translational anti‐infective pharmacology, including choice of appropriate measures of exposure and pharmacodynamic (PD) measures, patient subpopulations, and drug‐drug interactions. Examples are presented for state‐of‐the‐art, empiric, mechanistic, interdisciplinary, and real‐world evidence MIDD applications in the development of antibacterials (review of minimum inhibitory concentration‐based models, mechanism‐based pharmacokinetic/PD (PK/PD) models, PK/PD models of resistance, and immune response), antifungals, antivirals, drugs for the treatment of global health infectious diseases, and medical countermeasures. The degree of adoption of MIDD practices across the infectious diseases field is also summarized. The future application of MIDD in infectious diseases will progress along two planes; “depth” and “breadth” of MIDD methods. “MIDD depth” refers to deeper incorporation of the specific pathogen biology and intrinsic and acquired‐resistance mechanisms; host factors, such as immunologic response and infection site, to enable deeper interrogation of pharmacological impact on pathogen clearance; clinical outcome and emergence of resistance from a pathogen; and patient and population perspective. In particular, improved early assessment of the emergence of resistance potential will become a greater focus in MIDD, as this is poorly mitigated by current development approaches. “MIDD breadth” refers to greater adoption of model‐centered approaches to anti‐infective development. Specifically, this means how various MIDD approaches and translational tools can be integrated or connected in a systematic way that supports decision making by key stakeholders (sponsors, regulators, and payers) across the entire development pathway.

The Treatment of Tuberculosis

Peloquin

Davies

2021

Tuberculosis (TB) remains a leading cause of infectious death worldwide, and poverty is a major driver. Clinically, TB presents as “latent” TB and active TB disease, and the treatment for each is different. TB drugs can display “early bactericidal activity (EBA)” and / or “sterilizing activity” (clearing persisters). Isoniazid is excellent at the former, and rifampin is excellent at the latter. Pyrazinamide and ethambutol complete the first‐line regimen for drug‐susceptible TB, each playing a specific role. Drug‐resistant TB is an increasing concern, being met, in part, with repurposed drugs (including moxifloxacin, levofloxacin, linezolid, clofazimine, and beta‐lactams) and new drugs (including bedaquiline, pretomanid, and delamanid). One challenge is to select drugs without overlapping adverse drug reaction profiles. QTc interval prolongation is one such concern, but to date, it has been manageable. Drug penetration into organism sanctuaries, such as the central nervous system, bone, and pulmonary TB cavities remain important challenges. The pharmacodynamics of most TB drugs can be described by the area under the curve (AUC) divided by the minimal inhibitory concentration (MIC). The hollow fiber infection model (HFIM) and various animal models (especially mouse and macaque) allow for sophisticated pharmacokinetic/pharmacodynamic experiments. These experiments may hasten the selection of the most potent, shortest possible regimens to treat even extremely drug resistant TB. These findings can be translated to humans by optimizing drug exposure in each patient, using therapeutic drug monitoring and dose individualization.