Evaluation of pharmacokinetic-pharmacodynamic relationships and selection of drug combinations for tuberculosis.

AIMS: Despite evidence of the efficacy of anti-tubercular drug regimens in clinical practice, the rationale underpinning the selection of doses and companion drugs for combination therapy remains empirical. Novel methods are needed to optimise the antibacterial activity in combination therapies. A drug-disease modelling framework for rational selection of dose and drug combinations in tuberculosis is presented here. METHODS: A model-based meta-analysis was performed to assess the antibacterial activity of different combinations in infected mice. Data retrieved from the published literature were analysed using a two-state bacterial growth dynamics model, including fast- and slow-growing bacterial populations. The contribution of each drug to the overall antibacterial activity of the combination was parameterised as relative change to the potency of the backbone drug (EC50 -F and/or EC50 -S). Rifampicin and bedaquiline were selected as paradigm drugs to evaluate the predictive performance of the modelling approach. RESULTS: Pyrazinamide increased the potency (EC50 -F and EC50 -S) of rifampicin (RZ) and bedaquiline (BZ) by almost two-fold. By contrast, pretomanid and isoniazid were found to worsen the antibacterial activity of BZ and RZ, respectively. Following extrapolation of in vivo pharmacokinetic-pharmacodynamic relationships, the dose of rifampicin showing maximum bactericidal effect in tuberculosis patients was predicted to be 70 mg·kg-1 when given in combination with pyrazinamide. CONCLUSIONS: The use of a drug-disease modelling framework may provide a more robust rationale for extrapolation and selection of dose and companion drugs in humans. Our analysis demonstrates that RZ and BZ should be considered as a backbone therapy in prospective novel combination regimens against tuberculosis.

First-Time-in-Human Study and Prediction of Early Bactericidal Activity for GSK3036656, a Potent Leucyl-tRNA Synthetase Inhibitor for Tuberculosis Treatment.

This first-time-in-human (FTIH) study evaluated the safety, tolerability, pharmacokinetics, and food effect of single and repeat oral doses of GSK3036656, a leucyl-tRNA synthetase inhibitor. In part A, GSK3036656 single doses of 5 mg (fed and fasted), 15 mg, and 25 mg and placebo were administered. In part B, repeat doses of 5 and 15 mg and placebo were administered for 14 days once daily. GSK3036656 showed dose-proportional increase following single-dose administration and after dosing for 14 days. The maximum concentration of drug in serum (Cmax) and area under the concentration-time curve from 0 h to the end of the dosing period (AUC0-τ) showed accumulation with repeated administration of approximately 2- to 3-fold. Pharmacokinetic parameters were not altered in the presence of food. Unchanged GSK3036656 was the only drug-related component detected in plasma and accounted for approximately 90% of drug-related material in urine. Based on total drug-related material detected in urine, the minimum absorbed doses after single (25 mg) and repeat (15 mg) dosing were 50 and 78%, respectively. Unchanged GSK3036656 represented at least 44% and 71% of the 25- and 15-mg doses, respectively. Clinical trial simulations were performed to guide dose escalation during the FTIH study and to predict the GSK3036656 dose range that produces the highest possible early bactericidal activity (EBA0-14) in the prospective phase II trial, with consideration of the predefined exposure limit. GSK3036656 was well tolerated after single and multiple doses, with no reports of serious adverse events. (This study has been registered at ClinicalTrials.gov under identifier NCT03075410.).

How long will treatment guidelines for TB continue to overlook variability in drug exposure?

BACKGROUND: Despite wide clinical acceptance, the use of weight-banded dosing regimens for the treatment of TB in adults has been defined on an empirical basis. The potential impact of known covariate factors on exposure to different drugs has not been taken into account. OBJECTIVES: To evaluate the effect of demographic factors on the exposure to standard of care drugs after weight-banded dosing, as currently recommended by TB treatment guidelines. In addition, we aim to identify alternative dosing regimens that ensure comparable systemic exposure across the overall patient population. METHODS: Clinical trial simulations were performed to assess the differences in systemic exposure in a cohort of virtual patients. Secondary pharmacokinetic parameters were used to evaluate the adequacy of each regimen along with the percentage of patients achieving predefined thresholds. RESULTS: Our results show that patients weighing less than 40 kg are underexposed relative to patients with higher body weight. The opposite trend was observed following a crude weight band-based dosing regimen with 50 kg as the cut-off point. Simulations indicate that a fixed-dose regimen based on three (<40 kg), four (40-70 kg) or five (>70 kg) tablets of 150 mg rifampicin, 75 mg isoniazid, 400 mg pyrazinamide and 275 mg ethambutol reduces variability in exposure, increasing the overall probability of favourable long-term outcome across the population. CONCLUSIONS: These findings suggest the need to revisit current guidelines for the dose of standard of care drugs for TB treatment in adults. The proposed fixed-dose regimen should be considered in future clinical trials.

Application of quantitative protein mass spectrometric data in the early predictive analysis of membrane-bound target engagement by monoclonal antibodies.

Model-informed drug discovery advocates the use of mathematical modeling and simulation for improved efficacy in drug discovery. In the case of monoclonal antibodies (mAbs) against cell membrane antigens, this requires quantitative insight into the target tissue concentration levels. Protein mass spectrometry data are often available but the values are expressed in relative, rather than in molar concentration units that are easier to incorporate into pharmacokinetic models. Here, we present an empirical correlation that converts the parts per million (ppm) concentrations in the PaxDb database to their molar equivalents that are more suitable for pharmacokinetic modeling. We evaluate the insight afforded to target tissue distribution by analyzing the likely tumor-targeting accuracy of mAbs recognizing either epidermal growth factor receptor or its homolog HER2. Surprisingly, the predicted tissue concentrations of both these targets exceed the Kd values of their respective therapeutic mAbs. Physiologically based pharmacokinetic (PBPK) modeling indicates that in these conditions only about 0.05% of the dosed mAb is likely to reach the solid tumor target cells. The rest of the dose is eliminated in healthy tissues via both nonspecific and target-mediated processes. The presented approach allows evaluation of the interplay between the target expression level in different tissues that determines the overall pharmacokinetic properties of the drug and the fraction that reaches the cells of interest. This methodology can help to evaluate the efficacy and safety properties of novel drugs, especially if the off-target cell degradation has cytotoxic outcomes, as in the case of antibody-drug conjugates.

Application of quantitative protein mass spectrometric data in the early predictive analysis of target engagement by monoclonal antibodies.

Model-informed drug discovery is endorsed by the US Food and Drug Administration (FDA) to improve the flow of medicines from bench to bedside. In the case of monoclonal antibodies, this necessitates taking into account not only the pharmacokinetic (PK) properties of the drug, but also the tissue distribution, concentration, and turnover of the target to guide dose and affinity selection, as well as serve as a link to downstream pharmacology. Relevant information (e.g., tissue proteomic data from quantitative mass spectrometry), is increasingly available from public domain data repositories, although not necessarily in the form that is directly usable for the purpose of quantitative, predictive, and mechanistic PK/pharmacodynamic (PD) modeling based on molarity or similar frameworks instead. Using secreted plasma protein concentrations measured both by immunochemical methods and mass spectrometry, we addressed this gap and derived an optimized nonlinear empirical function that establishes the correlation between the two data sets and validated the approach taken using a wider data set of all proteins found in plasma. In addition, we present a semimechanistic framework for the plasma half-life of soluble proteins where clearance is expressed as a nonlinear function of the molecular weight of the protein. Finally, we apply the approach to two established therapeutic antibody targets: complement factor C5 and PCSK9 to demonstrate how the described framework can be applied to predictive PK/PD modeling.

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.

Prediction of lung exposure to anti-tubercular drugs using plasma pharmacokinetic data: Implications for dose selection.

The development of novel candidate molecules for tuberculosis remains challenging, as drug distribution into the target tissue is not fully characterised in preclinical models of infection. Often antitubercular human dose selection is derived from pharmacokinetic data in plasma. Here, we explore whether whole-body physiologically-based pharmacokinetic (PBPK) modelling enables the prediction of lung exposure to anti-tubercular drugs in humans. Whole-body PBPK models were developed for rifampicin, isoniazid, pyrazinamide, and ethambutol using plasma data in mice as basis for the prediction of lung exposure. Model parameters were subsequently used to extrapolate disposition properties from mouse and determine lung:plasma ratio in humans. Model predictions were compared to biopsy data from patients. Predictions were deemed adequate if they fell within two-fold range of the observations. The concentration vs time profiles in lung were adequately predicted in mice. Isoniazid and pyrazinamide lung exposures were predicted to be comparable to plasma levels, whereas ethambutol lung exposure was predicted to be higher than in plasma. Lung:plasma ratio in humans could be reasonably predicted from preclinical data, but was highly dependent on the distribution model. This analysis showed that plasma pharmacokinetics may be used in conjunction with PBPK modelling to derive lung tissue exposure in mice and humans during early lead optimisation phase. However, the impact of uncertainty in predicted tissue exposure due to distribution should be always investigated through a sensitivity analysis when only plasma data is available. Despite these limitations, insight into lung tissue distribution represents a critical step for the dose rationale in tuberculosis patients.

Discovery of a first-in-class reversible DNMT1-selective inhibitor with improved tolerability and efficacy in acute myeloid leukemia.

DNA methylation, a key epigenetic driver of transcriptional silencing, is universally dysregulated in cancer. Reversal of DNA methylation by hypomethylating agents, such as the cytidine analogs decitabine or azacytidine, has demonstrated clinical benefit in hematologic malignancies. These nucleoside analogs are incorporated into replicating DNA where they inhibit DNA cytosine methyltransferases DNMT1, DNMT3A and DNMT3B through irreversible covalent interactions. These agents induce notable toxicity to normal blood cells thus limiting their clinical doses. Herein we report the discovery of GSK3685032, a potent first-in-class DNMT1-selective inhibitor that was shown via crystallographic studies to compete with the active-site loop of DNMT1 for penetration into hemi-methylated DNA between two CpG base pairs. GSK3685032 induces robust loss of DNA methylation, transcriptional activation and cancer cell growth inhibition in vitro. Due to improved in vivo tolerability compared with decitabine, GSK3685032 yields superior tumor regression and survival mouse models of acute myeloid leukemia.

The implications of model-informed drug discovery and development for tuberculosis.

Despite promising advances in the field and highly efficacious first-line treatment, an estimated 9.6 million people are still infected with tuberculosis (TB). Innovative methods are required to effectively transition the growing number of compounds into novel combination regimens. However, progression of compounds into patients occurs despite the lack of clear understanding of the pharmacokinetic-pharmacodynamic (PKPD) relationships. The PreDiCT-TB consortium was established in response to the existing gaps in TB drug development. The aim of the consortium is to develop new preclinical tools in concert with an in silico model-based approach, grounded in PKPD principles. Here, we highlight the potential impact of such an integrated framework on the various stages of TB drug development and on the dose rationale for drug combinations.