Mechanistic Modeling of Mycobacterium tuberculosis Infection in Murine Models for Drug and Vaccine Efficacy Studies

Zhang, Nan; Strydom, Natasha; Tyagi, Sandeep; Soni, Heena; Tasneen, Rokeya; Nuermberger, Eric L.; Savic, Rada

doi:10.1128/aac.01727-19

Cited by 19 publications

(23 citation statements)

References 36 publications

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“…1c ), consistent with rapid bacillary replication. After the onset of adaptive immunity 19 , 20 , the Mtb burden plateaued and rRNA synthesis slowed (day 25). By day 53, the RS ratio was low but far from maximally suppressed, consistent with previous “replication clock” experiments showing that the plateau in CFU reflects a dynamic equilibrium between death and ongoing replication rather than a dormant non-replicating state 19 , 21 , 22 .…”

Section: Resultsmentioning

confidence: 99%

Mycobacterium tuberculosis precursor rRNA as a measure of treatment-shortening activity of drugs and regimens

et al. 2021

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There is urgent need for new drug regimens that more rapidly cure tuberculosis (TB). Existing TB drugs and regimens vary in treatment-shortening activity, but the molecular basis of these differences is unclear, and no existing assay directly quantifies the ability of a drug or regimen to shorten treatment. Here, we show that drugs historically classified as sterilizing and non-sterilizing have distinct impacts on a fundamental aspect of Mycobacterium tuberculosis physiology: ribosomal RNA (rRNA) synthesis. In culture, in mice, and in human studies, measurement of precursor rRNA reveals that sterilizing drugs and highly effective drug regimens profoundly suppress M. tuberculosis rRNA synthesis, whereas non-sterilizing drugs and weaker regimens do not. The rRNA synthesis ratio provides a readout of drug effect that is orthogonal to traditional measures of bacterial burden. We propose that this metric of drug activity may accelerate the development of shorter TB regimens.

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Section: Resultsmentioning

confidence: 99%

Mycobacterium tuberculosis precursor rRNA as a measure of treatment-shortening activity of drugs and regimens

et al. 2021

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“…The immune system forms a natural component in models of viral infections, such as HIV, 57 but has also been considered in models for translation of other infections. In tuberculosis, the adaptive immune system is important for bacterial clearance, and has in a modeling framework, based on data from a mouse model, been suggested to affect the replication rate, 58,59 and be dependent on both the current bacterial load and the incubation time. This model was subsequently applied to perform simulations to predict long-term treatment outcomes in patients.…”

Section: Host Responsementioning

confidence: 99%

Pivotal Role of Translation in Anti‐Infective Development

Friberg

2021

Clin Pharma and Therapeutics

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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.

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“…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%

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

Rayner

Smith

Andes

et al. 2021

Clin Pharma and Therapeutics

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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.

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Mechanistic Modeling of Mycobacterium tuberculosis Infection in Murine Models for Drug and Vaccine Efficacy Studies

Cited by 19 publications

References 36 publications

Mycobacterium tuberculosis precursor rRNA as a measure of treatment-shortening activity of drugs and regimens

Mycobacterium tuberculosis precursor rRNA as a measure of treatment-shortening activity of drugs and regimens

Pivotal Role of Translation in Anti‐Infective Development

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

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