Assessing Pharmacodynamic Interactions in Mice Using the Multistate Tuberculosis Pharmacometric and General Pharmacodynamic Interaction Models

Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges

Polypharmacy is commonly employed in clinical settings. The potential risks of drug-drug interactions (DDIs) can compromise efficacy and pose serious health hazards. Integrating pharmacokinetics (PK) and pharmacodynamics (PD) models into DDIs research provides a reliable method for evaluating and optimizing drug regimens. With advancements in our comprehension of both individual drug mechanisms and DDIs, conventional models have begun to evolve towards more detailed and precise directions, especially in terms of the simulation and analysis of physiological mechanisms. Selecting appropriate models is crucial for an accurate assessment of DDIs. This review details the theoretical frameworks and quantitative benchmarks of PK and PD modeling in DDI evaluation, highlighting the establishment of PK/PD modeling against a backdrop of complex DDIs and physiological conditions, and further showcases the potential of quantitative systems pharmacology (QSP) in this field. Furthermore, it explores the current advancements and challenges in DDI evaluation based on models, emphasizing the role of emerging in vitro detection systems, high-throughput screening technologies, and advanced computational resources in improving prediction accuracy.

Extrapolation of lung pharmacokinetics of antitubercular drugs from preclinical species to humans using PBPK modelling

OBJECTIVES

To develop physiologically based pharmacokinetic (PBPK) models for widely used anti-TB drugs, namely rifampicin, pyrazinamide, isoniazid, ethambutol and moxifloxacin lung pharmacokinetics (PK)-regarding both healthy and TB-infected tissue (cellular lesion and caseum)-in preclinical species and to extrapolate to humans.

METHODS

Empirical models were used for the plasma PK of each species, which were connected to multicompartment permeability-limited lung models within a middle-out PBPK approach with an appropriate physiological parameterization that was scalable across species. Lung's extracellular water (EW) was assumed to be the linking component between healthy and infected tissue, while passive diffusion was assumed for the drug transferring between cellular lesion and caseum.

RESULTS

In rabbits, optimized unbound fractions in intracellular water of rifampicin, moxifloxacin and ethambutol were 0.015, 0.056 and 0.08, respectively, while the optimized unbound fractions in EW of pyrazinamide and isoniazid in mice were 0.25 and 0.17, respectively. In humans, all mean extrapolated daily AUC and Cmax values of various lung regions were within 2-fold of the observed ones. Unbound concentrations in the caseum were lower than unbound plasma concentrations for both rifampicin and moxifloxacin. For rifampicin, unbound concentrations in cellular rim are slightly lower, while for moxifloxacin they are significantly higher than unbound plasma concentrations.

CONCLUSIONS

The developed PBPK approach was able to extrapolate lung PK from preclinical species to humans and to predict unbound concentrations in the various TB-infected regions, unlike empirical lung models. We found that plasma free drug PK is not always a good surrogate for TB-infected tissue unbound PK.

A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis

Introduction: Understanding drug exposure at disease target sites is pivotal to profiling new drug candidates in terms of tolerability and efficacy. Such quantification is particularly tedious for anti-tuberculosis (TB) compounds as the heterogeneous pulmonary microenvironment due to the infection may alter lung permeability and affect drug disposition. Murine models have been a longstanding support in TB research so far and are here used as human surrogates to unveil the distribution of several anti-TB compounds at the site-of-action via a novel and centralized PBPK design framework. Methods: As an intermediate approach between data-driven pharmacokinetic (PK) models and whole-body physiologically based (PB) PK models, we propose a parsimonious framework for PK investigation (minimal PBPK approach) that retains key physiological processes involved in TB disease, while reducing computational costs and prior knowledge requirements. By lumping together pulmonary TB-unessential organs, our minimal PBPK model counts 9 equations compared to the 36 of published full models, accelerating the simulation more than 3-folds in Matlab 2022b. Results: The model has been successfully tested and validated against 11 anti-TB compounds-rifampicin, rifapentine, pyrazinamide, ethambutol, isoniazid, moxifloxacin, delamanid, pretomanid, bedaquiline, OPC-167832, GSK2556286 - showing robust predictability power in recapitulating PK dynamics in mice. Structural inspections on the proposed design have ensured global identifiability and listed free fraction in plasma and blood-to-plasma ratio as top sensitive parameters for PK metrics. The platform-oriented implementation allows fast comparison of the compounds in terms of exposure and target attainment. Discrepancies in plasma and lung levels for the latest BPaMZ and HPMZ regimens have been analyzed in terms of their impact on preclinical experiment design and on PK/PD indices. Conclusion: The framework we developed requires limited drug- and species-specific information to reconstruct accurate PK dynamics, delivering a unified viewpoint on anti-TB drug distribution at the site-of-action and a flexible fit-for-purpose tool to accelerate model-informed drug design pipelines and facilitate translation into the clinic.

An improved PKPD modeling approach to characterize the pharmacodynamic interaction over time between ceftazidime/avibactam and colistin from in vitro time-kill experiments against multidrug-resistant Klebsiella pneumoniae isolates

In contrast to the checkerboard method, bactericidal experiments [time-kill curves (TKCs)] allow an assessment of pharmacodynamic (PD) interactions over time. However, TKCs in combination pose interpretation problems. The objective of this study was to characterize the PD interaction over time between ceftazidime/avibactam (CZA) and colistin (CST) using TKC against four multidrug-resistant Klebsiella pneumoniae susceptible to both antibiotics and expressing a widespread carbapenemase determinant KPC-3. In vitro TKCs were performed and analyzed using pharmacokinetic/pharmacodynamic (PKPD) modeling. The general pharmacodynamic interaction model was used to characterize PD interactions between drugs. The 95% confidence intervals (95%CIs) of the expected additivity and of the observed interaction were built using parametric bootstraps and compared to evaluate the in vitro PD interaction over time. Further simulations were conducted to investigate the effect of the combination at varying concentrations typically observed in patients. Regrowth was observed in TKCs at high concentrations of drugs alone [from 4 to 32× minimum inhibitory concentrations (MIC)], while the combination systematically prevented the regrowth at concentrations close to the MIC. Significant synergy or antagonism were observed under specific conditions but overall 95%CIs overlapped widely over time indicating an additive interaction between antibiotics. Moreover, simulations of typical PK profile at standard dosages indicated that the interaction should be additive in clinical conditions. The nature of the PD interaction varied with time and concentration in TKC. Against the four K. pneumoniae isolates, the bactericidal effect of CZA + CST combination was predicted to be additive and to prevent the emergence of resistance at clinical concentrations.

Evaluation of Acute and Sub-Acute Toxicity, Oxidative Stress and Molecular Docking of Two Nitrofuranyl Amides as Promising Anti-Tuberculosis Agents

Tuberculosis (TB) remains a widespread infectious disease and one of the top 10 causes of death worldwide. Nevertheless, despite significant advances in the development of new drugs against tuberculosis, many therapies and preventive measures do not lead to the expected favorable health results for various reasons. The aim of this study was to evaluate the acute and sub-acute toxicity and oxidative stress of two selected nitrofuranyl amides with high in vitro antimycobacterial activity. In addition, molecular docking studies were performed on both compounds to elucidate the possibilities for further development of new anti-tuberculosis candidates with improved efficacy, selectivity, and pharmacological parameters. Acute toxicity tests showed that no changes were observed in the skin, coat, eyes, mucous membranes, secretions, and vegetative activity in mice. The histological findings include features consistent with normal histological architecture without being associated with concomitant pathological conditions. The observed oxidative stress markers indicated that the studied compounds disturbed the oxidative balance in the mouse liver. Based on the molecular docking, compound DO-190 showed preferable binding energies compared to DO-209 in three out of four targets, while both compounds showed promising protein-ligand interactions. Thus, both studied compounds displayed promising activity with low toxicity and can be considered for further evaluation and/or lead optimization.

In Vivo Toxicity, Redox-Modulating Capacity and Intestinal Permeability of Novel Aroylhydrazone Derivatives as Anti-Tuberculosis Agents

The emergence and spread of Mycobacterium tuberculosis strains resistant to many or all anti-tuberculosis (TB) drugs require the development of new compounds both efficient and with minimal side effects. Structure-activity-toxicity relationships of such novel, structurally diverse compounds must be thoroughly elucidated before further development. Here, we present the aroylhydrazone compounds (3a and 3b) regarding their: (i) acute and subacute toxicity in mice; (ii) redox-modulating in vivo and in vitro capacity; (iii) pathomorphology in the liver, kidney, and small intestine tissue specimens; and (iv) intestinal permeability. The acute toxicity test showed that the two investigated compounds exhibited low toxicity by oral and intraperitoneal administration. Changes in behavior, food amount, and water intake were not observed during 14 days of the oral administration at two doses of 1/10 and 1/20 of the LD₅₀. The histological examination of the different tissue specimens did not show toxic changes. The in vitro antioxidant assays confirmed the ex vivo results. High gastrointestinal tract permeability at all tested pH values were demonstrated for both compounds. To conclude, both compounds 3a and 3b are highly permeable with low toxicity and can be considered for further evaluation and/or lead optimization.

Pharmacokinetics and pharmacodynamics of anti-tuberculosis drugs: An evaluation of in vitro, in vivo methodologies and human studies

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.

Bacterial growth dynamics and pharmacokinetic-pharmacodynamic relationships of rifampicin and bedaquiline in BALB/c mice

BACKGROUND AND PURPOSE

Translational efforts in the evaluation of novel anti-tubercular drugs demand better integration of pharmacokinetic-pharmacodynamic data arising from preclinical protocols. However, parametric approaches that discriminate drug effect from the underlying bacterial growth dynamics have not been fully explored, making it difficult to translate and/or extrapolate preclinical findings to humans. This analysis aims to develop a drug-disease model that allows distinction between drug- and system-specific properties.

EXPERIMENTAL APPROACH

Given their clinical relevance, rifampicin and bedaquiline were used as test compounds. A two-state model was used to describe bacterial growth dynamics. The approach assumes the existence of fast- and slow-growing bacterial populations. Drug effect on the growth dynamics of each subpopulation was characterised in terms of potency (EC₅₀ -F and EC₅₀ -S) and maximum killing rate.

KEY RESULTS

The doubling time of the fast- and slow-growing population was estimated to be 25 h and 42 days, respectively. Rifampicin was more potent against the fast-growing (EC₅₀ -F = 4.8 mg·L^-1 ), as compared with the slow-growing population (EC₅₀ -S = 60.2 mg·L^-1 ). Bedaquiline showed higher potency than rifampicin (EC₅₀ -F = 0.19 mg·L^-1 ; EC₅₀ -S = 3.04 mg·L^-1 ). External validation procedures revealed an effect of infection route on the apparent potency of rifampicin.

CONCLUSION AND IMPLICATIONS

Model parameter estimates suggest that nearly maximum killing rate is achieved against fast-growing, but not against slow-growing populations at the tested doses. Evidence of differences in drug potency for each subpopulation may facilitate the translation of preclinical findings and improve the dose rationale for anti-tubercular drugs in humans.

Can phenotypic data complement our understanding of antimycobacterial effects for drug combinations?

Objectives

To demonstrate how phenotypic cell viability data can provide insight into antimycobacterial effects for the isoniazid/rifampicin treatment backbone.

Methods

Data from a Mycobacterium komossense hollow-fibre infection model comprising a growth control group, rifampicin at three different exposures (C_max = 0.14, 0.4 and 1.47 mg/L with t_½ = 1.57 h and τ = 8 h) and rifampicin plus isoniazid (C_max rifampicin = 0.4 mg/L and C_max isoniazid = 1.2 mg/L with t_½ = 1.57 h and τ = 8 h) were used for this investigation. A non-linear mixed-effects modelling approach was used to fit conventional cfu data, quantified using solid-agar plating. Phenotypic proportions of respiring (alive), respiring but with damaged cell membrane (injured) and ‘not respiring’ (dead) cells data were quantified using flow cytometry and Sytox Green™ (Sigma–Aldrich, UK) and resazurin sodium salt staining and fitted using a multinomial logistic regression model.

Results

Isoniazid/rifampicin combination therapy displayed a decreasing overall antimicrobial effect with time (θTime1/2 = 438 h) on cfu data, in contrast to rifampicin monotherapy where this trend was absent. In the presence of isoniazid a phenotype associated with cell injury was displayed, whereas with rifampicin monotherapy a pattern of phenotypic cell death was observed. Bacterial killing onset time on cfu data correlated negatively (θTime50 = 28.9 h, θLAGRIF50 = 0.132 mg/L) with rifampicin concentration up to 0.165 mg/L and this coincided with a positive relationship between rifampicin concentration and the probability of phenotypic cell death.

Conclusions

Cell viability data provide structured information on the pharmacodynamic interaction between isoniazid and rifampicin that complements the understanding of the antibacillary effects of this mycobacterial treatment backbone.

Difference in persistent tuberculosis bacteria between in vitro and sputum from patients: implications for translational predictions

This study aimed to investigate the number of persistent bacteria in sputum from tuberculosis patients compared to in vitro and to suggest a model-based approach for accounting for the potential difference. Sputum smear positive patients (n = 25) provided sputum samples prior to onset of chemotherapy. The number of cells detected by conventional agar colony forming unit (CFU) and most probable number (MPN) with Rpf supplementation were quantified. Persistent bacteria was assumed to be the difference between MPN_rpf and CFU. The difference in persistent bacteria between in vitro and human sputum prior to chemotherapy was quantified using different model-based approaches. The persistent bacteria in sputum was 17% of the in vitro levels, suggesting a difference in phenotypic resistance, whereas no difference was found for multiplying bacterial subpopulations. Clinical trial simulations showed that the predicted time to 2 log fall in MPN_rpf in a Phase 2a setting using in vitro pre-clinical efficacy information, would be almost 3 days longer if drug response was predicted ignoring the difference in phenotypic resistance. The discovered phenotypic differences between in vitro and humans prior to chemotherapy could have implications on translational efforts but can be accounted for using a model-based approach for translating in vitro to human drug response.

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

Tuberculosis (TB) kills more people than any other infectious disease. Challenges for developing better treatments include the complex pathology due to within-host immune dynamics, interpatient variability in disease severity and drug pharmacokinetics-pharmacodynamics (PK-PD), and the growing emergence of resistance. Model-informed drug development using quantitative and translational pharmacology has become increasingly recognized as a method capable of drug prioritization and regimen optimization to efficiently progress compounds through TB drug development phases. In this review, we examine translational models and tools, including plasma PK scaling, site-of-disease lesion PK, host-immune and bacteria interplay, combination PK-PD models of multidrug regimens, resistance formation, and integration of data across nonclinical and clinical phases.We propose a workflow that integrates these tools with computational platforms to identify drug combinations that have the potential to accelerate sterilization, reduce relapse rates, and limit the emergence of resistance.

Longitudinal Pharmacokinetic-Pharmacodynamic Biomarkers Correlate With Treatment Outcome in Drug-Sensitive Pulmonary Tuberculosis: A Population Pharmacokinetic-Pharmacodynamic Analysis

Background

This study aims to explore relationships between baseline demographic covariates, plasma antibiotic exposure, sputum bacillary load, and clinical outcome data to help improve future tuberculosis (TB) treatment response predictions.

Methods

Data were available from a longitudinal cohort study in Malawian drug-sensitive TB patients on standard therapy, including steady-state plasma antibiotic exposure (154 patients), sputum bacillary load (102 patients), final outcome (95 patients), and clinical details. Population pharmacokinetic and pharmacokinetic-pharmacodynamic models were developed in the software package NONMEM. Outcome data were analyzed using univariate logistic regression and Cox proportional hazard models in R, a free software for statistical computing.

Results

Higher isoniazid exposure correlated with increased bacillary killing in sputum (P < .01). Bacillary killing in sputum remained fast, with later progression to biphasic decline, in patients with higher rifampicin area under the curve (AUC)_0-24 (P < .01). Serial sputum colony counting negativity at month 2 (P < .05), isoniazid C_MAX (P < .05), isoniazid C_MAX/minimum inhibitory concentration ([MIC] P < .01), and isoniazid AUC_0-24/MIC (P < .01) correlated with treatment success but not with remaining free of TB. Slower bacillary killing (P < .05) and earlier progression to biphasic bacillary decline (P < .01) both correlate with treatment failure. Posttreatment recurrence only correlated with slower bacillary killing (P < .05).

Conclusions

Patterns of early bacillary clearance matter. Static measurements such as month 2 sputum conversion and pharmacokinetic parameters such as C_MAX/MIC and AUC_0-24/MIC were predictive of treatment failure, but modeling of quantitative longitudinal data was required to assess the risk of recurrence. Pooled individual patient data analyses from larger datasets are needed to confirm these findings.

A model-based analysis identifies differences in phenotypic resistance between in vitro and in vivo: implications for translational medicine within tuberculosis

Proper characterization of drug effects on Mycobacterium tuberculosis relies on the characterization of phenotypically resistant bacteria to correctly establish exposure–response relationships. The aim of this work was to evaluate the potential difference in phenotypic resistance in in vitro compared to murine in vivo models using CFU data alone or CFU together with most probable number (MPN) data following resuscitation with culture supernatant. Predictions of in vitro and in vivo phenotypic resistance i.e. persisters, using the Multistate Tuberculosis Pharmacometric (MTP) model framework was evaluated based on bacterial cultures grown with and without drug exposure using CFU alone or CFU plus MPN data. Phenotypic resistance and total bacterial number in in vitro natural growth observations, i.e. without drug, was well predicted by the MTP model using only CFU data. Capturing the murine in vivo total bacterial number and persisters during natural growth did however require re-estimation of model parameter using both the CFU and MPN observations implying that the ratio of persisters to total bacterial burden is different in vitro compared to murine in vivo. The evaluation of the in vitro rifampicin drug effect revealed that higher resolution in the persister drug effect was seen using CFU and MPN compared to CFU alone although drug effects on the other bacterial populations were well predicted using only CFU data. The ratio of persistent bacteria to total bacteria was predicted to be different between in vitro and murine in vivo. This difference could have implications for subsequent translational efforts in tuberculosis drug development.

Drug Effect of Clofazimine on Persisters Explains an Unexpected Increase in Bacterial Load in Patients

Antituberculosis (anti-TB) drug development is dependent on informative trials to secure the development of new antibiotics and combination regimens. Clofazimine (CLO) and pyrazinamide (PZA) are important components of recommended standard multidrug treatments of TB. Paradoxically, in a phase IIa trial aiming to define the early bactericidal activity (EBA) of CLO and PZA monotherapy over the first 14 days of treatment, no significant drug effect was demonstrated for the two drugs using traditional statistical analysis.

Antituberculosis (anti-TB) drug development is dependent on informative trials to secure the development of new antibiotics and combination regimens. Clofazimine (CLO) and pyrazinamide (PZA) are important components of recommended standard multidrug treatments of TB. Paradoxically, in a phase IIa trial aiming to define the early bactericidal activity (EBA) of CLO and PZA monotherapy over the first 14 days of treatment, no significant drug effect was demonstrated for the two drugs using traditional statistical analysis. Using a model-based analysis, we characterized the statistically significant exposure-response relationships for both drugs that could explain the original findings of an increase in the numbers of CFU with CLO treatment and no effect with PZA. Sensitive analyses are crucial for exploring drug effects in early clinical trials to make the right decisions for advancement to further development. We propose that this quantitative semimechanistic approach provides a rational framework for analyzing phase IIa EBA studies and can accelerate anti-TB drug development.

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

Translational Model-Informed Approach for Selection of Tuberculosis Drug Combination Regimens in Early Clinical Development

The development of optimal treatment regimens in tuberculosis (TB) remains challenging due to the need of combination therapy and possibility of pharmacodynamic (PD) interactions. Preclinical information about PD interactions needs to be used more optimally when designing early bactericidal activity (EBA) studies. In this work, we developed a translational approach which can allow for forward translation to predict efficacy of drug combination in EBA studies using the Multistate Tuberculosis Pharmacometric (MTP) and the General Pharmacodynamic Interaction (GPDI) models informed by in vitro static time-kill data. These models were linked with translational factors to account for differences between the in vitro system and humans. Our translational MTP-GPDI model approach was able to predict the EBA_{0-2 days} , EBA_{0-5 days} , and EBA_{0-14 days} from different EBA studies of rifampicin and isoniazid in monotherapy and combination. Our translational model approach can contribute to an optimal dose selection of drug combinations in early TB clinical trials.

A model-informed preclinical approach for prediction of clinical pharmacodynamic interactions of anti-TB drug combinations

Background

Identification of pharmacodynamic interactions is not reasonable to carry out in a clinical setting for many reasons. The aim of this work was to develop a model-informed preclinical approach for prediction of clinical pharmacodynamic drug interactions in order to inform early anti-TB drug development.

Methods

In vitro time–kill experiments were performed with Mycobacterium tuberculosis using rifampicin, isoniazid or ethambutol alone as well as in different combinations at clinically relevant concentrations. The multistate TB pharmacometric (MTP) model was used to characterize the natural growth and exposure–response relationships of each drug after mono exposure. Pharmacodynamic interactions during combination exposure were characterized by linking the MTP model to the general pharmacodynamic interaction (GPDI) model with successful separation of the potential effect on each drug’s potency (EC₅₀) by the combining drug(s).

Results

All combinations showed pharmacodynamic interactions at cfu level, where all combinations, except isoniazid plus ethambutol, showed more effect (synergy) than any of the drugs alone. Using preclinical information, the MTP-GPDI modelling approach was shown to correctly predict clinically observed pharmacodynamic interactions, as deviations from expected additivity.

Conclusions

With the ability to predict clinical pharmacodynamic interactions, using preclinical information, the MTP-GPDI model approach outlined in this study constitutes groundwork for model-informed input to the development of new and enhancement of existing anti-TB combination regimens.

Forecasting Clinical Dose-Response From Preclinical Studies in Tuberculosis Research: Translational Predictions With Rifampicin

A crucial step for accelerating tuberculosis drug development is bridging the gap between preclinical and clinical trials. In this study, we developed a preclinical model-informed translational approach to predict drug effects across preclinical systems and early clinical trials using the in vitro-based Multistate Tuberculosis Pharmacometric (MTP) model using rifampicin as an example. The MTP model predicted rifampicin biomarker response observed in 1) a hollow-fiber infection model, 2) a murine study to determine pharmacokinetic/pharmacodynamic indices, and 3) several clinical phase IIa early bactericidal activity (EBA) studies. In addition, we predicted rifampicin biomarker response at high doses of up to 50 mg/kg, leading to an increased median EBA0-2 days (90% prediction interval) of 0.513 log CFU/mL/day (0.310; 0.701) compared to the standard dose of 10 mg/kg of 0.181 log/CFU/mL/day (0.076; 0.483). These results suggest that the translational approach could assist in the selection of drugs and doses in early-phase clinical tuberculosis trials.

Comparisons of Analysis Methods for Assessment of Pharmacodynamic Interactions Including Design Recommendations

Quantitative evaluation of potential pharmacodynamic (PD) interactions is important in tuberculosis drug development in order to optimize Phase 2b drug selection and ultimately to define clinical combination regimens. In this work, we used simulations to (1) evaluate different analysis methods for detecting PD interactions between two hypothetical anti-tubercular drugs in in vitro time-kill experiments, and (2) provide design recommendations for evaluation of PD interactions. The model used for all simulations was the Multistate Tuberculosis Pharmacometric (MTP) model linked to the General Pharmacodynamic Interaction (GPDI) model. Simulated data were re-estimated using the MTP-GPDI model implemented in Bliss Independence or Loewe Additivity, or using a conventional model such as an Empirical Bliss Independence-based model or the Greco model based on Loewe Additivity. The GPDI model correctly characterized different PD interactions (antagonism, synergism, or asymmetric interaction), regardless of the underlying additivity criterion. The commonly used conventional models were not able to characterize asymmetric PD interactions, i.e., concentration-dependent synergism and antagonism. An optimized experimental design was developed that correctly identified interactions in ≥ 94% of the evaluated scenarios using the MTP-GPDI model approach. The MTP-GPDI model approach was proved to provide advantages to other conventional models for assessing PD interactions of anti-tubercular drugs and provides key information for selection of drug combinations for Phase 2b evaluation.

A general pharmacodynamic interaction model identifies perpetrators and victims in drug interactions

Assessment of pharmacodynamic (PD) drug interactions is a cornerstone of the development of combination drug therapies. To guide this venture, we derive a general pharmacodynamic interaction (GPDI) model for ≥2 interacting drugs that is compatible with common additivity criteria. We propose a PD interaction to be quantifiable as multidirectional shifts in drug efficacy or potency and explicate the drugs’ role as victim, perpetrator or even both at the same time. We evaluate the GPDI model against conventional approaches in a data set of 200 combination experiments in Saccharomyces cerevisiae: 22% interact additively, a minority of the interactions (11%) are bidirectional antagonistic or synergistic, whereas the majority (67%) are monodirectional, i.e., asymmetric with distinct perpetrators and victims, which is not classifiable by conventional methods. The GPDI model excellently reflects the observed interaction data, and hence represents an attractive approach for quantitative assessment of novel combination therapies along the drug development process.

Assessment of pharmacodynamic interactions is at the heart of combination therapy development. Here the authors introduce a general drug interaction scoring model that enables quantification of synergistic and antagonistic interactions and determination of the directionality of the interactions.

Semi-mechanistic pharmacokinetic-pharmacodynamic modelling of antibiotic drug combinations

BACKGROUND

Deriving suitable dosing regimens for antibiotic combination therapy poses several challenges as the drug interaction can be highly complex, the traditional pharmacokinetic-pharmacodynamic (PKPD) index methodology cannot be applied straightforwardly, and exploring all possible dose combinations is unfeasible. Therefore, semi-mechanistic PKPD models developed based on in vitro single and combination experiments can be valuable to suggest suitable combination dosing regimens.

AIMS

To outline how the interaction between two antibiotics has been characterized in semi-mechanistic PKPD models. We also explain how such models can be applied to support dosing regimens and design future studies.

SOURCES

PubMed search for published semi-mechanistic PKPD models of antibiotic drug combinations.

CONTENT

Thirteen publications were identified where ten had applied subpopulation synergy to characterize the combined effect, i.e. independent killing rates for each drug and bacterial subpopulation. We report the various types of interaction functions that have been used to describe the combined drug effects and that characterized potential deviations from additivity under the PKPD model. Simulations from the models had commonly been performed to compare single versus combined dosing regimens and/or to propose improved dosing regimens.

IMPLICATIONS

Semi-mechanistic PKPD models allow for integration of knowledge on the interaction between antibiotics for various PK and PD profiles, and can account for associated variability within the population as well as parameter uncertainty. Decisions on suitable combination regimens can thereby be facilitated. We find the application of semi-mechanistic PKPD models to be essential for efficient development of antibiotic combination regimens that optimize bacterial killing and/or suppress resistance development.