A physiologically based pharmacokinetic model of rifampin in mice

Parameter grouping and co-estimation in physiologically based kinetic models using genetic algorithms

Physiologically based kinetic (PBK) models are widely used in pharmacology and toxicology for predicting the internal disposition of substances upon exposure, voluntarily or not. Due to their complexity, a large number of model parameters need to be estimated, either through in silico tools, in vitro experiments, or by fitting the model to in vivo data. In the latter case, fitting complex structural models on in vivo data can result in overparameterization and produce unrealistic parameter estimates. To address these issues, we propose a novel parameter grouping approach, which reduces the parametric space by co-estimating groups of parameters across compartments. Grouping of parameters is performed using genetic algorithms and is fully automated, based on a novel goodness-of-fit metric. To illustrate the practical application of the proposed methodology, two case studies were conducted. The first case study demonstrates the development of a new PBK model, while the second focuses on model refinement. In the first case study, a PBK model was developed to elucidate the biodistribution of titanium dioxide (TiO2) nanoparticles in rats following intravenous injection. A variety of parameter estimation schemes were employed. Comparative analysis based on goodness-of-fit metrics demonstrated that the proposed methodology yields models that outperform standard estimation approaches, while utilizing a reduced number of parameters. In the second case study, an existing PBK model for perfluorooctanoic acid (PFOA) in rats was extended to incorporate additional tissues, providing a more comprehensive portrayal of PFOA biodistribution. Both models were validated through independent in vivo studies to ensure their reliability.

Rifampin- and Silymarin-Mediated Pharmacokinetic Interactions of Exogenous and Endogenous Substrates in a Transgenic OATP1B Mouse Model

Organic anion-transporting polypeptides (OATP) 1B1 and OATP1B3, encoded by the SLCO gene family of the solute carrier superfamily, are involved in the disposition of many exogenous and endogenous compounds. Preclinical rodent models help assess risks of pharmacokinetic interactions, but interspecies differences in transporter orthologs and expression limit direct clinical translation. An OATP1B transgenic mouse model comprising a rodent Slco1a/1b gene cluster knockout and human SLCO1B1 and SLCO1B3 gene insertions provides a potential physiologically relevant preclinical tool to predict pharmacokinetic interactions. Pharmacokinetics of exogenous probe substrates, pitavastatin and pravastatin, and endogenous OATP1B biomarkers, coproporphyrin-I and coproporphyrin-III, were determined in the presence and absence of known OATP/Oatp inhibitors, rifampin or silymarin (an extract of milk thistle [Silybum marianum]), in wild-type FVB mice and humanized OATP1B mice. Rifampin increased exposure of pitavastatin (4.6- and 2.8-fold), pravastatin (3.6- and 2.2-fold), and coproporphyrin-III (1.6- and 2.1-fold) in FVB and OATP1B mice, respectively, but increased coproporphyrin-I AUC0-24h only (1.8-fold) in the OATP1B mice. Silymarin did not significantly affect substrate AUC, likely because the silymarin flavonolignan concentrations were at or below their reported IC50 values for the relevant OATPs/Oatps. Silymarin increased the Cmax of pitavastatin 2.7-fold and pravastatin 1.9-fold in the OATP1B mice. The data of the OATP1B mice were similar to those of the pitavastatin and pravastatin clinical data; however, the FVB mice data more closely recapitulated pitavastatin clinical data than the data of the OATP1B mice, suggesting that the OATP1B mice are a reasonable, though costly, preclinical strain for predicting pharmacokinetic interactions when doses are optimized to achieve clinically relevant plasma concentrations.

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.

A physiologically-based pharmacokinetic model for tuberculosis drug disposition at extrapulmonary sites

Tuberculosis (TB) is a leading cause of mortality attributed to an infectious agent. TB primarily targets the lungs, but in about 16% cases can affect other organs as well, giving rise to extrapulmonary TB (EPTB). However, an optimal regimen for EPTB treatment is not defined. Although the recommended treatment for most forms of EPTB is the same as pulmonary TB, the pharmacokinetics of EPTB therapy are not as well studied. To address this gap, we formulate a whole-body physiologically-based pharmacokinetic (PBPK) model for EPTB that for the first time includes the ability to simulate drug concentrations in the pleura and lymph node, the most commonly affected sites of EPTB. Using this model, we estimate the time-dependent concentrations, at potential EPTB infection sites, of the following four first-line anti-TB drugs: rifampicin, ethambutol, isoniazid, and pyrazinamide. We use reported plasma concentration kinetics data to estimate model parameters for each drug and validate our model using reported concentration data not used for model formulation or parameter estimation. Model predictions match the validation data, and reported pharmacokinetic parameters (maximum plasma concentration, time to reach maximum concentration) for the drugs. The model also predicts ethambutol, isoniazid, and pyrazinamide concentrations in the pleura that match reported experimental values from an independent study. For each drug, the predicted drug concentrations at EPTB sites are compared with their critical concentration. Simulations suggest that although rifampicin and isoniazid concentrations are greater than critical concentration values at most EPTB sites, the concentrations of ethambutol and pyrazinamide are lower than their critical concentrations at most EPTB sites.

A Comparative Analysis of Physiologically Based Pharmacokinetic Models for Human Immunodeficiency Virus and Tuberculosis Infections

Physiologically based pharmacokinetic (PBPK) models have gained in popularity in the last decade in both drug development and regulatory science. PBPK models differ from classical pharmacokinetic models in that they include specific compartments for tissues involved in exposure, toxicity, biotransformation, and clearance processes connected by blood flow. This study aimed to address the gaps between the mathematics and pharmacology framework observed in the literature. These gaps included nonconserved systems of equations and compartment concentration that were not biologically relatable to the tissues of interest. The resulting system of nonlinear differential equations is solved numerically with various methods for benchmarking and comparison. Furthermore, a sensitivity analysis of all parameters were conducted to elucidate the critical parameters of the model. The resulting model was fit to clinical data as a performance benchmark. The clinical data captured the second line of antiretroviral treatment, lopinavir and ritonavir. The model and clinical data correlate well for coadministration of lopinavir/ritonavir with rifampin. Drug-drug interaction was captured between lopinavir and rifampin. This article provides conclusions about the suitability of physiologically based pharmacokinetic models for the prediction of drug-drug interaction and antiretroviral and anti-TB pharmacokinetics.

Streamlining physiologically‐based pharmacokinetic model design for intravenous delivery of nanoparticle drugs

Physiologically‐based pharmacokinetic (PBPK) modeling for nanoparticles elucidates the nanoparticle drug’s disposition in the body and serves a vital role in drug development and clinical studies. This paper offers a systematic and tutorial‐like approach to developing a model structure and writing distribution ordinary differential equations based on asking binary questions involving the physicochemical nature of the drug in question. Further, by synthesizing existing knowledge, we summarize pertinent aspects in PBPK modeling and create a guide for building model structure and distribution equations, optimizing nanoparticle and non‐nanoparticle specific parameters, and performing sensitivity analysis and model validation. The purpose of this paper is to facilitate a streamlined model development process for students and practitioners in the field.

In silico study on radiobiological efficacy of Ac-225 and Lu-177 for PSMA-guided radiotherapy

The good efficacy of radioligand therapy (RLT) targeting prostate specific-membrane antigen (PSMA) for the treatment of metastatic castration-resistant prostate cancer (mCRPC) has been recently demonstrated in several clinical studies. However, the treatment effect of ¹⁷⁷Lu-PSMA-ligands is still suboptimal for a significant fraction of patients. In contrast to external beam radiotherapy, the radiation dose distribution itself is strongly influenced by the heterogeneous tumour microenvironment. Although microdosimetry is critical for RLT treatment outcome, it is difficult to clinically or experimentally establish the quantitative relation. We propose an in silico approach to quantitatively investigate the microdosimetry and its influence on treatment outcome for PSMA-directed RLT of two different radioisotopes ¹⁷⁷Lu and ²²⁵ Ac. The ultimate goal is optimize the combined ¹⁷⁷ Lu and ²²⁵ Ac-PSMA therapy and maximize the anti-tumour effect, while minimizing irradiation of off-target tissues.Clinical relevance- With the proposed hybrid model we show that ¹⁷⁷Lu-PSMA-ligands treatment assures a more homogeneously distributed dose and a lower dependency of the treatment outcome on the domain vascularisation. On the other hand, the ²²⁵Ac-PSMA-ligands treatment shows a much stronger efficacy in killing tumor cells with an equivalent mean dose distribution even in an hypoxic environment.

Is Hypoxia a Factor Influencing PSMA-Directed Radioligand Therapy?-An In Silico Study on the Role of Chronic Hypoxia in Prostate Cancer

Simple Summary

Tumor hypoxia is considered a critical factor associated with the resistance of conventional radiotherapy, where the X-ray-induced free radicals lead to DNA damage in a manner that is strongly dependent on the tissue oxygenation. The emerging PSMA-directed radioligand therapy (RLT) employs the α or β particles emitted by the radiopharmaceuticals to kill the tumor cells. In contrast to conventional therapy, the induced DNA damage is less dependent on the oxygenation status. Less attention has been paid to investigating whether tumor hypoxia will influence the efficacy of PSMA-directed RLT. We propose a histology-driven in silico model to quantitatively investigate the influence of tumor hypoxia on the treatment outcome for PSMA-directed RLT with 177Lu and 225Ac. Our finding suggests that hypoxia is a factor to be considered for the application of PSMA-directed RLT.

Abstract

Radioligand therapy (RLT) targeting prostate specific-membrane antigen (PSMA) is an emerging treatment for metastatic castration-resistant prostate cancer (mCRPC). It administrates 225Ac- or 177Lu-labeled ligands for the targeted killing of tumor cells. Differently from X- or γ-ray, for the emitted α or β particles the ionization of the DNA molecule is less dependent on the tissue oxygenation status. Furthermore, the diffusion range of electrons in a tumor is much larger than the volume typically spanned by hypoxic regions. Therefore, hypoxia is less investigated as an influential factor for PSMA-directed RLT, in particular with β emitters. This study proposes an in silico approach to theoretically investigate the influence of tumor hypoxia on the PSMA-directed RLT. Based on mice histology images, the distribution of the radiopharmaceuticals was simulated with an in silico PBPK-based convection–reaction–diffusion model. Three anti-CD31 immunohistochemistry slices were used to simulate the tumor microenvironment. Ten regions of interest with varying hypoxia severity were analyzed. A kernel-based method was developed for dose calculation. The cell survival probability was calculated according to the linear-quadratic model. The statistical analysis performed on all the regions of interest (ROIs) shows more heterogeneous dose distributions obtained with 225Ac compared to 177Lu. The higher homogeneity of 177Lu-PSMA-ligand treatment is due to the larger range covered by the emitted β particles. The dose-to-tissue histogram (DTH) metric shows that in poorly vascularized ROIs only 10% of radiobiological hypoxic tissue receives the target dose using 177Lu-PSMA-ligand treatment. This percentage drops down to 5% using 225Ac. In highly vascularized ROIs, the percentage of hypoxic tissue receiving the target dose increases to more than 85% and 65% for the 177Lu and 225Ac-PSMA-ligands, respectively. The in silico study demonstrated that the reduced vascularization of the tumor strongly influences the dose delivered by PSMA-directed RLT, especially in hypoxic regions and consequently the treatment outcome.

Elucidating the Antimycobacterial Mechanism of Action of Decoquinate Derivative RMB041 Using Metabolomics

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), still remains one of the leading causes of death from a single infectious agent worldwide. The high prevalence of this disease is mostly ascribed to the rapid development of drug resistance to the current anti-TB drugs, exacerbated by lack of patient adherence due to drug toxicity. The aforementioned highlights the urgent need for new anti-TB compounds with different antimycobacterial mechanisms of action to those currently being used. An N-alkyl quinolone; decoquinate derivative RMB041, has recently shown promising antimicrobial activity against Mtb, while also exhibiting low cytotoxicity and excellent pharmacokinetic characteristics. Its exact mechanism of action, however, is still unknown. Considering this, we used GCxGC-TOFMS and well described metabolomic approaches to analyze and compare the metabolic alterations of Mtb treated with decoquinate derivative RMB041 by comparison to non-treated Mtb controls. The most significantly altered pathways in Mtb treated with this drug include fatty acid metabolism, amino acid metabolism, glycerol metabolism, and the urea cycle. These changes support previous findings suggesting this drug acts primarily on the cell wall and secondarily on the DNA metabolism of Mtb. Additionally, we identified metabolic changes suggesting inhibition of protein synthesis and a state of dormancy.

Predicting the Disposition of the Antimalarial Drug Artesunate and Its Active Metabolite Dihydroartemisinin Using Physiologically Based Pharmacokinetic Modeling

Artemisinin-based combination therapies (ACTs) have proven to be effective in helping to combat the global malaria epidemic. To optimally apply these drugs, information about their tissue-specific disposition is required, and one approach to predict these pharmacokinetic characteristics is physiologically based pharmacokinetic (PBPK) modeling. In this study, a whole-body PBPK model was developed to simulate the time-dependent tissue concentrations of artesunate (AS) and its active metabolite, dihydroartemisinin (DHA). The model was developed for both rats and humans and incorporated drug metabolism of the parent compound and major metabolite. Model calibration was conducted using data from the literature in a Bayesian framework, and model verification was assessed using separate sets of data. Results showed good agreement between model predictions and the validation data, demonstrating the capability of the model in predicting the blood, plasma, and tissue pharmacokinetics of AS and DHA. It is expected that such a tool will be useful in characterizing the disposition of these chemicals and ultimately improve dosing regimens by enabling a quantitative assessment of the tissue-specific drug levels critical in the evaluation of efficacy and toxicity.

Pregnancy-specific physiologically-based toxicokinetic models for bisphenol A and bisphenol S

Predictions from physiologically based toxicokinetic (PBTK) models can help inform human health risk assessment for potentially toxic chemicals in the environment. Bisphenol S (BPS) is the second most abundant bisphenol detected in humans in the United States, after bisphenol A (BPA). We have recently demonstrated that BPS, much like BPA, can cross the placental barrier and disrupt placental function. Differences in physicochemical properties, toxicokinetics, and exposure outcomes between BPA and other bisphenols prevent direct extrapolation of existing BPA PBTK models to BPS. The current study aimed to develop pregnancy-specific PBTK (p-PBTK) models for BPA and BPS, using a common p-PBTK model structure. Novel paired maternal and fetal pregnancy data sets for total, unconjugated, and conjugated BPA and BPS plasma concentrations from three independent studies in pregnant sheep were used for model calibration. The nine-compartment (maternal blood, liver, kidney, fat, placenta and rest of body, and fetal liver, blood and rest of body) models simulated maternal and fetal experimental data for both BPA and BPS within one standard deviation for the majority of the experimental data points, highlighting the robustness of both models. Simulations were run to examine fetal exposure following daily maternal exposure to BPA or BPS at their tolerable daily intake dose over a two-week period. These predictive simulations show fetal accumulation of both bisphenols over time. Interestingly, the steady-state approximation following this dosing strategy achieved a fetal concentration of unconjugated BPA to levels observed in cord blood from human biomonitoring studies. These models advance our understanding of bisphenolic compound toxicokinetics during pregnancy and may be used as a quantitative comparison tool in future p-PBTK models for related chemicals.

Design, Synthesis, and Pharmacological Evaluation of Potent Positive Allosteric Modulators of the Glucagon-like Peptide-1 Receptor (GLP-1R)

The therapeutic success of peptidic GLP-1 receptor agonists for treatment of type 2 diabetes mellitus (T2DM) motivated our search for orally bioavailable small molecules that can activate the GLP-1 receptor (GLP-1R) as a well-validated target for T2DM. Here, the discovery and characterization of a potent and selective positive allosteric modulator (PAM) for GLP-1R based on a 3,4,5,6-tetrahydro-1H-1,5-epiminoazocino[4,5-b]indole scaffold is reported. Optimization of this series from HTS was supported by a GLP-1R ligand binding model. Biological in vitro testing revealed favorable ADME and pharmacological profiles for the best compound 19. Characterization by in vivo pharmacokinetic and pharmacological studies demonstrated that 19 activates GLP-1R as positive allosteric modulator (PAM) in the presence of the much less active endogenous degradation product GLP1(9-36)NH₂ of the potent endogenous ligand GLP-1(7-36)NH₂. While these data suggest the potential of small molecule GLP-1R PAMs for T2DM treatment, further optimization is still required towards a clinical candidate.

An in vitro ADME and in vivo Pharmacokinetic Study of Novel TB-Active Decoquinate Derivatives

Tuberculosis (TB) is currently the leading cause of mortality due to an infectious disease, despite the existence of multiple effective first-line and second-line drugs. The current anti-TB regimen requires a prolonged treatment period of around 6 months and is only efficacious against drug-sensitive strains of Mycobacterium tuberculosis (Mtb). With a rise in cases of multi-drug resistant and extensively drug resistant strains of Mtb, newer treatments comprising compounds with novel mechanisms of action are required. Although decoquinate (DQ) is inactive against Mtb, its derivatives are of interest to anti-TB drug discovery because of their potential to permeate the mycobacterial cell wall, Mtb-infected macrophages, and granulomatous lesions by passive diffusion. The compounds also display mechanisms of action which are unlike those of currently used quinolones, potentially displaying activity against new targets. Three such derivatives bearing an alkyl group at N-1 and an amide group at C-3 (RMB 041, -043, and -073) displayed potent in vitro activities against Mtb H37Rv (90% minimum inhibitory concentrations, MIC90 = 1.61, 4.18, and 1.88 μM, respectively) and high selectivity indices (10-25). In this study, we evaluated the drug-like properties (in vitro microsomal stability, microsomal/plasma protein binding, kinetic solubility, lipophilicity, and passive permeability) and pharmacokinetic (PK) parameters of these compounds after intravenous and oral administration to male C57BL/6 mice. The compounds showed markedly improved kinetic solubilities compared to that of the parental DQ and were metabolically stable in vitro. The maximum concentrations reached after oral administration were 5.4 ± 0.40, 5.6 ± 1.40, and 2.0 ± 0.03 μM; elimination half-lives were 23.4 ± 2.50, 6.2 ± 0.80, and 11.6 ± 1.30 h; and bioavailabilities were 21.4 ± 1.0, 22.1 ± 2.2, and 5.9 ± 1.3 for RMB041, -043, and -073, respectively. These compounds therefore display promising drug-like properties, and their PK/toxicity profiles (including long half-lives both in vitro and in vivo) support their potential as candidates for further investigation in animal models of Mtb infection.

PBTK model for assessment of operator exposure to haloxyfop using human biomonitoring and toxicokinetic data

Physiologically-based toxicokinetic (PBTK) models are mathematical representations of chemical absorption, distribution, metabolism and excretion (ADME) in animals. Each parameter in a PBTK model describes a physiological, physicochemical or biochemical process that affects ADME. Distributions can be assigned to the model parameters to describe population variability and uncertainty. In this study to assess potential crop sprayer operator exposure to the herbicide haloxyfop, a permeability-limited PBTK model was constructed with parameter uncertainty and variability, and calibrated using Bayesian analysis via Markov chain Monte Carlo methods. A hierarchical statistical model was developed to reconstruct operator exposure using available measurement data: experimentally determined octanol/water partition coefficient, mouse and human toxicokinetic data as well as human biomonitoring data from seven operators who participated in a field study. A chemical risk assessment was performed by comparing the estimated systemic exposure to the acceptable operator exposure level (AOEL). The analysis suggested that in one of the seven operators, the model estimates systemic exposure to haloxyfop of 49.04 ± 10.19 SD μg/kg bw in relation to an AOEL of 5.0 μg/kg bw/day. This does not represent a safety concern as this predicted exposure is well within the 100-fold uncertainty factor applied to the No Observed Adverse Effect Level (NOAEL) in animals. In addition, given the availability of human toxicokinetic data, the 10x uncertainty factor for interspecies differences in ADME could be reduced (EFSA, 2006). Thus the AOEL could potentially be raised tenfold from 5.0 to 50.0 μg/kg bw/day.

Synergistic Antimicrobial Activity of Colistin in Combination with Rifampin and Azithromycin against Escherichia coli Producing MCR-1

The lack of available antibiotics is a global public health problem due to the emergence of antimicrobial resistance. Effective therapeutic regimens are urgently needed against Escherichia coli strains that produce the colistin resistance gene mcr-1 and to inhibit the emergence of resistance. In this study, we assessed the antimicrobial activity of a series of concentrations of colistin-based combinations with rifampin and/or azithromycin against three strains of Escherichia coli, including colistin-resistant isolate MZ1501^R, isolate HE1704^R that produces MCR-1, and colistin-susceptible isolate MZ1509^S Experiments were conducted with a medium inoculum of ∼10⁷ CFU/ml over 48 h. Subsequently, the in vivo therapeutic effect was investigated using a neutropenic mouse thigh infection model. Almost all monotherapies showed unsatisfactory antibacterial activity against E. coli isolates producing MCR-1. In contrast, colistin in combination with rifampin or azithromycin resulted in an obvious decrease in the bacterial burden albeit with regrowth. More obviously, synergistic antimicrobial activity of colistin-based triple-combination therapy with rifampin and azithromycin was observed, resulting in a rapid and exhaustive antibacterial effect. In vivo treatments confirmed these findings, where mean decreases of 0.38 to 0.90 log₁₀ CFU and 1.27 to 1.78 log₁₀ CFU were noted after 24 h and 48 h of treatment, respectively, against colistin-resistant E. coli strains when 5 mg/kg of body weight of colistin was combined with rifampin and azithromycin. Colistin-based combinations with rifampin and azithromycin provide a more active therapeutic regimen than monotherapy or colistin-based double combinations against E. coli producing MCR-1.

A quantitative systems pharmacology (QSP) model for Pneumocystis treatment in mice

BACKGROUND

The yeast-like fungi Pneumocystis, resides in lung alveoli and can cause a lethal infection known as Pneumocystis pneumonia (PCP) in hosts with impaired immune systems. Current therapies for PCP, such as trimethoprim-sulfamethoxazole (TMP-SMX), suffer from significant treatment failures and a multitude of serious side effects. Novel therapeutic approaches (i.e. newly developed drugs or novel combinations of available drugs) are needed to treat this potentially lethal opportunistic infection. Quantitative Systems Pharmacological (QSP) models promise to aid in the development of novel therapies by integrating available pharmacokinetic (PK) and pharmacodynamic (PD) knowledge to predict the effects of new treatment regimens.

RESULTS

In this work, we constructed and independently validated PK modules of a number of drugs with available pharmacokinetic data. Characterized by simple structures and well constrained parameters, these PK modules could serve as a convenient tool to summarize and predict pharmacokinetic profiles. With the currently accepted hypotheses on the life stages of Pneumocystis, we also constructed a PD module to describe the proliferation, transformation, and death of Pneumocystis. By integrating the PK module and the PD module, the QSP model was constrained with observed levels of asci and trophic forms following treatments with multiple drugs. Furthermore, the temporal dynamics of the QSP model were validated with corresponding data.

CONCLUSIONS

We developed and validated a QSP model that integrates available data and promises to facilitate the design of future therapies against PCP.

Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling

Traditionally, the solution to reduce parameter dimensionality in a physiologically-based pharmacokinetic (PBPK) model is through expert judgment. However, this approach may lead to bias in parameter estimates and model predictions if important parameters are fixed at uncertain or inappropriate values. The purpose of this study was to explore the application of global sensitivity analysis (GSA) to ascertain which parameters in the PBPK model are non-influential, and therefore can be assigned fixed values in Bayesian parameter estimation with minimal bias. We compared the elementary effect-based Morris method and three variance-based Sobol indices in their ability to distinguish “influential” parameters to be estimated and “non-influential” parameters to be fixed. We illustrated this approach using a published human PBPK model for acetaminophen (APAP) and its two primary metabolites APAP-glucuronide and APAP-sulfate. We first applied GSA to the original published model, comparing Bayesian model calibration results using all the 21 originally calibrated model parameters (OMP, determined by “expert judgment”-based approach) vs. the subset of original influential parameters (OIP, determined by GSA from the OMP). We then applied GSA to all the PBPK parameters, including those fixed in the published model, comparing the model calibration results using this full set of 58 model parameters (FMP) vs. the full set influential parameters (FIP, determined by GSA from FMP). We also examined the impact of different cut-off points to distinguish the influential and non-influential parameters. We found that Sobol indices calculated by eFAST provided the best combination of reliability (consistency with other variance-based methods) and efficiency (lowest computational cost to achieve convergence) in identifying influential parameters. We identified several originally calibrated parameters that were not influential, and could be fixed to improve computational efficiency without discernable changes in prediction accuracy or precision. We further found six previously fixed parameters that were actually influential to the model predictions. Adding these additional influential parameters improved the model performance beyond that of the original publication while maintaining similar computational efficiency. We conclude that GSA provides an objective, transparent, and reproducible approach to improve the performance and computational efficiency of PBPK models.

Prediction of Deoxypodophyllotoxin Disposition in Mouse, Rat, Monkey, and Dog by Physiologically Based Pharmacokinetic Model and the Extrapolation to Human

Deoxypodophyllotoxin (DPT) is a potential anti-tumor candidate prior to its clinical phase. The aim of the study was to develop a physiologically based pharmacokinetic (PBPK) model consisting of 13 tissue compartments to predict DPT disposition in mouse, rat, monkey, and dog based on in vitro and in silico inputs. Since large interspecies difference was found in unbound fraction of DPT in plasma, we assumed that K_t:pl,u (unbound tissue-to-plasma concentration ratio) was identical across species. The predictions of our model were then validated by in vivo data of corresponding preclinical species, along with visual predictive checks. Reasonable matches were found between observed and predicted plasma concentrations and pharmacokinetic parameters in all four animal species. The prediction in the related seven tissues of mouse was also desirable. We also attempted to predict human pharmacokinetic profile by both the developed PBPK model and interspecies allometric scaling across mouse, rat and monkey, while dog was excluded from the scaling. The two approaches reached similar results. We hope the study will help in the efficacy and safety assessment of DPT in future clinical studies and provide a reference to the preclinical screening of similar compounds by PBPK model.

Physiologically Based Pharmacokinetic Model of Rifapentine and 25-Desacetyl Rifapentine Disposition in Humans

Rifapentine (RPT) is a rifamycin antimycobacterial and, as part of a combination therapy, is indicated for the treatment of pulmonary tuberculosis (TB) caused by Mycobacterium tuberculosis Although the results from a number of studies indicate that rifapentine has the potential to shorten treatment duration and enhance completion rates compared to other rifamycin agents utilized in antituberculosis drug regimens (i.e., regimens 1 to 4), its optimal dose and exposure in humans are unknown. To help inform such an optimization, a physiologically based pharmacokinetic (PBPK) model was developed to predict time course, tissue-specific concentrations of RPT and its active metabolite, 25-desacetyl rifapentine (dRPT), in humans after specified administration schedules for RPT. Starting with the development and verification of a PBPK model for rats, the model was extrapolated and then tested using human pharmacokinetic data. Testing and verification of the models included comparisons of predictions to experimental data in several rat tissues and time course RPT and dRPT plasma concentrations in humans from several single- and repeated-dosing studies. Finally, the model was used to predict RPT concentrations in the lung during the intensive and continuation phases of a current recommended TB treatment regimen. Based on these results, it is anticipated that the PBPK model developed in this study will be useful in evaluating dosing regimens for RPT and for characterizing tissue-level doses that could be predictors of problems related to efficacy or safety.

Investigation on rifampicin administration from the standpoint of pharmacokinetics/pharmacodynamics in a neutropenic murine thigh infection model

BACKGROUND

The purpose of this study was to examine the pharmacokinetics/pharmacodynamics of rifampicin against methicillin-susceptible Staphylococcus aureus (MSSA), methicillin-resistant S. aureus (MRSA), and vancomycin-intermediate S. aureus (VISA) in a neutropenic murine thigh infection model.

METHODS

Three S. aureus isolates (MSSA [ATCC 25923], MRSA and VISA [Mu50]) with rifampicin MIC 0.06 to >256 μg/mL were tested. The efficacy was calculated as the change in bacterial density. A maximum effect model was used to determine the PK/PD index that best described the dose-response data.

RESULTS

The area under the curve (AUC)/MIC and maximum concentration of drug in serum (Cmax/MIC) were the best correlated with in vivo efficacy (AUC/MIC, R(2) = 0.96; Cmax/MIC, R(2) = 0.97) for S. aureus ATCC 25923 strain, and the dose fractionation-response study did not show significantly different antimicrobial activity (p = 0.10). The AUC/MIC values associated with stasis and 1-log kill for the S. aureus ATCC 25923 strain were 386 and 952, respectively. On the other hand, no antimicrobial efficacy was observed against two strains (MRSA and VISA) with MIC of 128 μg/mL or more.

CONCLUSION

Rifampicin demonstrated concentration-dependent killing. The AUC/MIC was a predictive PK/PD index.

Role of enterohepatic recirculation in drug disposition: cooperation and complications

Enterohepatic recirculation (EHC) concerns many physiological processes and notably affects pharmacokinetic parameters such as plasma half-life and AUC as well as estimates of bioavailability of drugs. Also, EHC plays a detrimental role as the compounds/drugs are allowed to recycle. An in-depth comprehension of this phenomenon and its consequences on the pharmacological effects of affected drugs is important and decisive in the design and development of new candidate drugs. EHC of a compound/drug occurs by biliary excretion and intestinal reabsorption, sometimes with hepatic conjugation and intestinal deconjugation. EHC leads to prolonged elimination half-life of the drugs, altered pharmacokinetics and pharmacodynamics. Study of the EHC of any drug is complicated due to unavailability of the apposite model, sophisticated procedures and ethical concerns. Different in vitro and in vivo methods for studies in experimental animals and humans have been devised, each having its own merits and demerits. Involvement of the different transporters in biliary excretion, intra- and inter-species, pathological and biochemical variabilities obscure the study of the phenomenon. Modeling of drugs undergoing EHC has always been intricate and exigent models have been exploited to interpret the pharmacokinetic profiles of drugs witnessing multiple peaks due to EHC. Here, we critically appraise the mechanisms of bile formation, factors affecting biliary drug elimination, methods to estimate biliary excretion of drugs, EHC, multiple peak phenomenon and its modeling.

Validation of a simple HPLC-UV method for rifampicin determination in plasma: Application to the study of rifampicin arteriovenous concentration gradient

In clinical practice, rifampicin exposure is estimated from its concentration in venous blood samples. In this study, we hypothesized that differences in rifampicin concentration may exist between arterial and venous plasma. An HPLC-UV method for determining rifampicin concentration in plasma using rifapentine as an internal standard was validated. The method, which requires a simple protein precipitation procedure as sample preparation, was performed to compare venous and arterial plasma kinetics after a single therapeutic dose of rifampicin (8.6 mg/kg i.v, infused over 30 min) in baboons (n=3). The method was linear from 0.1 to 40 μg mL(-1) and all validation parameters fulfilled the international requirements. In baboons, rifampicin concentration in arterial plasma was higher than in venous plasma. Arterial Cmax was 2.1±0.2 fold higher than venous Cmax. The area under the curve (AUC) from 0 to 120 min was ∼80% higher in arterial plasma, indicating a significant arteriovenous concentration gradient in early rifampicin pharmacokinetics. Arterial and venous plasma concentrations obtained 6h after rifampicin injection were not different. An important arteriovenous equilibration delay for rifampicin pharmacokinetics is reported. Determination in venous plasma concentrations may considerably underestimate rifampicin exposure to organs during the distribution phase.

Development of a Multicompartment Permeability-Limited Lung PBPK Model and Its Application in Predicting Pulmonary Pharmacokinetics of Antituberculosis Drugs

Achieving sufficient concentrations of antituberculosis (TB) drugs in pulmonary tissue at the optimum time is still a challenge in developing therapeutic regimens for TB. A physiologically based pharmacokinetic model incorporating a multicompartment permeability-limited lung model was developed and used to simulate plasma and pulmonary concentrations of seven drugs. Passive permeability of drugs within the lung was predicted using an in vitro-in vivo extrapolation approach. Simulated epithelial lining fluid (ELF):plasma concentration ratios showed reasonable agreement with observed clinical data for rifampicin, isoniazid, ethambutol, and erythromycin. For clarithromycin, itraconazole and pyrazinamide the observed ELF:plasma ratios were significantly underpredicted. Sensitivity analyses showed that changing ELF pH or introducing efflux transporter activity between lung tissue and ELF can alter the ELF:plasma concentration ratios. The described model has shown utility in predicting the lung pharmacokinetics of anti-TB drugs and provides a framework for predicting pulmonary concentrations of novel anti-TB drugs.

Computational pharmacokinetics/pharmacodynamics of rifampin in a mouse tuberculosis infection model

One critical approach to preclinical evaluation of anti-tuberculosis (anti-TB) drugs is the study of correlations between drug exposure and efficacy in animal TB infection models. While such pharmacokinetic/pharmacodynamic (PK/PD) studies are useful for the identification of optimal clinical dosing regimens, they are resource intensive and are not routinely performed. A mathematical model capable of simulating the PK/PD properties of drug therapy for experimental TB offers a way to mitigate some of the practical obstacles to determining the PK/PD index that best correlates with efficacy. Here, we present a preliminary physiologically based PK/PD model of rifampin therapy in a mouse TB infection model. The computational framework integrates whole-body rifampin PKs, cell population dynamics for the host immune response to Mycobacterium tuberculosis infection, drug-bacteria interactions, and a Bayesian method for parameter estimation. As an initial application, we calibrated the model to a set of available rifampin PK/PD data and simulated a separate dose fractionation experiment for bacterial killing kinetics in the lungs of TB-infected mice. The simulation results qualitatively agreed with the experimentally observed PK/PD correlations, including the identification of area under the concentration-time curve as best correlating with efficacy. This single-drug framework is aimed toward extension to multiple anti-TB drugs in order to facilitate development of optimal combination regimens.

A multi-scale approach to designing therapeutics for tuberculosis

Approximately one third of the world's population is infected with Mycobacterium tuberculosis. Limited information about how the immune system fights M. tuberculosis and what constitutes protection from the bacteria impact our ability to develop effective therapies for tuberculosis. We present an in vivo systems biology approach that integrates data from multiple model systems and over multiple length and time scales into a comprehensive multi-scale and multi-compartment view of the in vivo immune response to M. tuberculosis. We describe computational models that can be used to study (a) immunomodulation with the cytokines tumor necrosis factor and interleukin 10, (b) oral and inhaled antibiotics, and

Sensitization of Gram-negative bacteria to rifampin and OAK combinations

While individually inefficient against Gram-negative bacteria, in-vitro combinations of rifampin and OAK were mutually synergistic since sub-minimal inhibitory concentrations of one compound have potentiated the other by 2–4 orders of magnitude. Synergy persisted in-vivo as single-dose systemic treatment of Klebsiella infected mice resulted in 10–20% versus 60% survival, respectively accomplished by individual and combined compounds. This outcome was achieved without drug formulation, rather, pharmacokinetic considerations have inspired the therapeutic regimen.

A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment

While active tuberculosis (TB) is a treatable disease, many complex factors prevent its global elimination. Part of the difficulty in developing optimal therapies is the large design space of antibiotic doses, regimens and combinations. Computational models that capture the spatial and temporal dynamics of antibiotics at the site of infection can aid in reducing the design space of costly and time-consuming animal pre-clinical and human clinical trials. The site of infection in TB is the granuloma, a collection of immune cells and bacteria that form in the lung, and new data suggest that penetration of drugs throughout granulomas is problematic. Here we integrate our computational model of granuloma formation and function with models for plasma pharmacokinetics, lung tissue pharmacokinetics and pharmacodynamics for two first line anti-TB antibiotics. The integrated model is calibrated to animal data. We make four predictions. First, antibiotics are frequently below effective concentrations inside granulomas, leading to bacterial growth between doses and contributing to the long treatment periods required for TB. Second, antibiotic concentration gradients form within granulomas, with lower concentrations toward their centers. Third, during antibiotic treatment, bacterial subpopulations are similar for INH and RIF treatment: mostly intracellular with extracellular bacteria located in areas non-permissive for replication (hypoxic areas), presenting a slowly increasing target population over time. Finally, we find that on an individual granuloma basis, pre-treatment infection severity (including bacterial burden, host cell activation and host cell death) is predictive of treatment outcome.

Optimization of the rifampin dosage to improve the therapeutic efficacy in tuberculosis treatment using a murine model

RATIONALE

The dosage of 10 mg/kg/d rifampin, as currently used in the treatment of tuberculosis (TB), is not an optimal dose. Shortening of treatment duration might be achievable using an increased rifampin dose.

OBJECTIVES

Determination of optimal rifampin dosage in mice, resulting in maximum therapeutic effect and without adverse effects. Assessment of associated pharmacokinetic parameters and pharmacokinetic/pharmacodynamic indices.

METHODS

A murine TB infection using a Beijing genotype Mycobacterium tuberculosis strain was established by intratracheal bacterial instillation followed by proper inhalation, while keeping mice in a vertical position. We assessed dose-dependent activity of rifampin in single-drug treatment during 3 weeks. The maximum tolerated dosage, pharmacokinetic parameters, and pharmacokinetic/pharmacodynamic index were determined. Therapeutic efficacy of a range of rifampin (R) dosages added to a regimen of isoniazid (H) and pyrazinamide (Z) was assessed.

MEASUREMENTS AND MAIN RESULTS

Maximum tolerated dosage of rifampin in the murine TB was 160 mg/kg/d. Pharmacokinetic measurement in HR(10)Z and HR(160)Z therapy regimens showed for rifampin a C(max) of 16.2 and 157.3 mg/L, an AUC(0-24h) of 132 and 1,782 h·mg/L, and AUC(0-24h)/minimum inhibitory concentration ratios of 528 and 7129, respectively. A clear dose-effect correlation was observed for rifampin after 3-week single-drug treatment. Administration of HR(80)Z allowed 9-week treatment duration to be effective without relapse of infection.

CONCLUSIONS

Our findings indicate that the currently used rifampin dosage in the therapy of TB is too low. In our murine TB model a rifampin dosage of 80 mg/kg/d enabled a significant reduction in therapy duration without adverse effects.