Model-based dose optimization framework for bedaquiline, pretomanid and linezolid for the treatment of drug-resistant tuberculosis.

AIMS: Bedaquiline, pretomanid and linezolid (BPaL) combination treatment against Mycobacterium tuberculosis is promising, yet safety and adherence concerns exist that motivate exploration of alternative dosing regimens. We developed a mechanistic modelling framework to compare the efficacy of the current and alternative BPaL treatment strategies. METHODS: Pharmacodynamic models for each drug in the BPaL combination treatment were developed using in vitro time-kill data. These models were combined with pharmacokinetic models, incorporating body weight, lesion volume, site-of-action distribution, bacterial susceptibility and pharmacodynamic interactions to assemble the framework. The model was qualified by comparing the simulations against the observed clinical data. Simulations were performed evaluating bedaquiline and linezolid approved (bedaquiline 400 mg once daily [QD] for 14 days followed by 200 mg three times a week, linezolid 1200 mg QD) and alternative dosing regimens (bedaquiline 200 mg QD, linezolid 600 mg QD). RESULTS: The framework adequately described the observed antibacterial activity data in patients following monotherapy for each drug and approved BPaL dosing. The simulations suggested a minor difference in median time to colony forming unit (CFU)-clearance state with the bedaquiline alternative compared to the approved dosing and the linezolid alternative compared to the approved dosing. Median time to non-replicating-clearance state was predicted to be 15 days from the CFU-clearance state. CONCLUSIONS: The model-based simulations suggested that comparable efficacy can be achieved using alternative bedaquiline and linezolid dosing, which may improve safety and adherence in drug-resistant tuberculosis patients. The framework can be utilized to evaluate treatment optimization approaches, including dosing regimen and duration of treatment predictions to eradicate both replicating- and non-replicating bacteria from lung and lesions.

Quantitative Systems Pharmacology Modeling Framework of Autophagy in Tuberculosis: Application to Adjunctive Metformin Host-Directed Therapy.

Quantitative systems pharmacology (QSP) modeling of the host immune response against Mycobacterium tuberculosis can inform the rational design of host-directed therapies (HDTs). We aimed to develop a QSP framework to evaluate the effects of metformin-associated autophagy induction in combination with antibiotics. A QSP framework for autophagy was developed by extending a model for host immune response to include adenosine monophosphate-activated protein kinase (AMPK)-mTOR-autophagy signaling. This model was combined with pharmacokinetic-pharmacodynamic models for metformin and antibiotics against M. tuberculosis. We compared the model predictions to mice infection experiments and derived predictions for the pathogen- and host-associated dynamics in humans treated with metformin in combination with antibiotics. The model adequately captured the observed bacterial load dynamics in mice M. tuberculosis infection models treated with metformin. Simulations for adjunctive metformin therapy in newly diagnosed patients suggested a limited yet dose-dependent effect of metformin on reduction of the intracellular bacterial load when the overall bacterial load is low, late during antibiotic treatment. We present the first QSP framework for HDTs against M. tuberculosis, linking cellular-level autophagy effects to disease progression and adjunctive HDT treatment response. This framework may be extended to guide the design of HDTs against M. tuberculosis.

Optimizing ethambutol dosing among HIV/tuberculosis co-infected patients: a population pharmacokinetic modelling and simulation study.

BACKGROUND: Reduced ethambutol serum concentrations are commonly observed among TB patients co-infected with HIV and may lead to treatment failure. OBJECTIVES: To perform a population pharmacokinetic study of ethambutol in HIV/TB patients, and to evaluate an intensified ethambutol weight-based dosing strategy to support pharmacokinetic target attainment. METHODS: We conducted a prospective study of ethambutol pharmacokinetics among HIV/TB patients administered first-line TB treatment in Botswana, with study visits before and after initiation of ART. Clinical and disease status markers, including HIV-associated systemic immune activation and gut dysfunction biomarkers, were evaluated as covariates of ethambutol pharmacokinetic parameters in non-linear mixed effects analysis. Monte Carlo simulations were performed to compare pharmacokinetic target attainment under standard and intensified weight-based ethambutol dosing strategies. RESULTS: We studied 40 HIV/TB patients prior to initiation of ART, of whom 24 returned for a second visit a median of 33 days following ART initiation. Ethambutol serum concentrations were best explained by a two-compartment model with first-order elimination, with a significant improvement in oral bioavailability following ART initiation. In Monte Carlo simulations, a supplementary ethambutol dose of 400 mg daily led to >2-fold improvements in pharmacokinetic target attainment probabilities in lung tissue, both before and after ART initiation. CONCLUSIONS: Low serum ethambutol concentrations were commonly observed among HIV/TB patients in Botswana, and the oral bioavailability of ethambutol increased following ART initiation. Supplementary ethambutol dosing among HIV/TB patients may provide a strategy to optimize anti-TB treatment regimens in this high-risk population.

Predictions of Bedaquiline Central Nervous System Exposure in Patients with Tuberculosis Meningitis Using Physiologically based Pharmacokinetic Modeling.

BACKGROUND AND OBJECTIVE: The use of bedaquiline as a treatment option for drug-resistant tuberculosis meningitis (TBM) is of interest to address the increased prevalence of resistance to first-line antibiotics. To this end, we describe a whole-body physiologically based pharmacokinetic (PBPK) model for bedaquiline to predict central nervous system (CNS) exposure. METHODS: A whole-body PBPK model was developed for bedaquiline and its metabolite, M2. The model included compartments for brain and cerebrospinal fluid (CSF). Model predictions were evaluated by comparison to plasma PK time profiles following different dosing regimens and sparse CSF concentrations data from patients. Simulations were then conducted to compare CNS and lung exposures to plasma exposure at clinically relevant dosing schedules. RESULTS: The model appropriately described the observed plasma and CSF bedaquiline and M2 concentrations from patients with pulmonary tuberculosis (TB). The model predicted a high impact of tissue binding on target site drug concentrations in CNS. Predicted unbound exposures within brain interstitial exposures were comparable with unbound vascular plasma and unbound lung exposures. However, unbound brain intracellular exposures were predicted to be 7% of unbound vascular plasma and unbound lung intracellular exposures. CONCLUSIONS: The whole-body PBPK model for bedaquiline and M2 predicted unbound concentrations in brain to be significantly lower than the unbound concentrations in the lung at clinically relevant doses. Our findings suggest that bedaquiline may result in relatively inferior efficacy against drug-resistant TBM when compared with efficacy against drug-resistant pulmonary TB.

Predictions of Bedaquiline and Pretomanid Target Attainment in Lung Lesions of Tuberculosis Patients using Translational Minimal Physiologically Based Pharmacokinetic Modeling.

BACKGROUND: Site-of-action concentrations for bedaquiline and pretomanid from tuberculosis patients are unavailable. The objective of this work was to predict bedaquiline and pretomanid site-of-action exposures using a translational minimal physiologically based pharmacokinetic (mPBPK) approach to understand the probability of target attainment (PTA). METHODS: A general translational mPBPK framework for the prediction of lung and lung lesion exposure was developed and validated using pyrazinamide site-of-action data from mice and humans. We then implemented the framework for bedaquiline and pretomanid. Simulations were conducted to predict site-of-action exposures following standard bedaquiline and pretomanid, and bedaquiline once-daily dosing. Probabilities of average concentrations within lesions and lungs greater than the minimum bactericidal concentration for non-replicating (MBCNR) and replicating (MBCR) bacteria were calculated. Effects of patient-specific differences on target attainment were evaluated. RESULTS: The translational modeling approach was successful in predicting pyrazinamide lung concentrations from mice to patients. We predicted that 94% and 53% of patients would attain bedaquiline average daily PK exposure within lesions (Cavg-lesion) > MBCNR during the extensive phase of bedaquiline standard (2 weeks) and once-daily (8 weeks) dosing, respectively. Less than 5% of patients were predicted to achieve Cavg-lesion > MBCNR during the continuation phase of bedaquiline or pretomanid treatment, and more than 80% of patients were predicted to achieve Cavg-lung >MBCR for all simulated dosing regimens of bedaquiline and pretomanid. CONCLUSIONS: The translational mPBPK model predicted that the standard bedaquiline continuation phase and standard pretomanid dosing may not achieve optimal exposures to eradicate non-replicating bacteria in most patients.

An Anecdote on Prospective Protein Targets for Developing Novel Plant Growth Regulators.

Phytohormones are the main regulatory molecules of core signalling networks associated with plant life cycle regulation. Manipulation of hormone signalling cascade enables the control over physiological traits of plant, which has major applications in field of agriculture and food sustainability. Hence, stable analogues of these hormones are long sought after and many of them are currently known, but the quest for more effective, stable and economically viable analogues is still going on. This search has been further strengthened by the identification of the components of signalling cascade such as receptors, downstream cascade members and transcription factors. Furthermore, many proteins of phytohormone cascades are available in crystallized forms. Such crystallized structures can provide the basis for identification of novel interacting compounds using in silico approach. Plenty of computational tools and bioinformatics software are now available that can aid in this process. Here, the metadata of all the major phytohormone signalling cascades are presented along with discussion on major protein-ligand interactions and protein components that may act as a potential target for manipulation of phytohormone signalling cascade. Furthermore, structural aspects of phytohormones and their known analogues are also discussed that can provide the basis for the synthesis of novel analogues.

Pharmacogenetic variability and the probability of site of action target attainment during tuberculosis meningitis treatment: A physiologically based pharmacokinetic modeling and simulations study.

OBJECTIVE AND METHODS: Our objective was to investigate the role of patient pharmacogenetic variability in determining site of action target attainment during tuberculous meningitis (TBM) treatment. Rifampin and isoniazid PBPK model that included SLCO1B1 and NAT2 effects on exposures respectively were obtained from literature, modified, and validated using available cerebrospinal-fluid (CSF) concentrations. Population simulations of isoniazid and rifampin concentrations in brain interstitial fluid and probability of target attainment according to genotypes and M. tuberculosis MIC levels, under standard and intensified dosing, were conducted. RESULTS: The rifampin and isoniazid model predicted steady-state drug concentration within brain interstitial fluid matched with the observed CSF concentrations. At MIC level of 0.25 mg/L, 57% and 23% of the patients with wild type and heterozygous SLCO1B1 genotype respectively attained the target in CNS with rifampin standard dosing, improving to 98% and 91% respectively with 35 mg/kg dosing. At MIC level of 0.25 mg/L, 33% of fast acetylators attained the target in CNS with isoniazid standard dosing, improving to 90% with 7.5 mg/kg dosing. CONCLUSION: In this study, the combined effects of pharmacogenetic and M. tuberculosis MIC variability were potent determinants of target attainment in CNS. The potential for genotype-guided dosing during TBM treatment should be further explored in prospective clinical studies.

Host-directed therapies for tuberculosis: quantitative systems pharmacology approaches.

Host-directed therapies (HDTs) that modulate host-pathogen interactions offer an innovative strategy to combat Mycobacterium tuberculosis (Mtb) infections. When combined with tuberculosis (TB) antibiotics, HDTs could contribute to improving treatment outcomes, reducing treatment duration, and preventing resistance development. Translation of the interplay of host-pathogen interactions leveraged by HDTs towards therapeutic outcomes in patients is challenging. Quantitative understanding of the multifaceted nature of the host-pathogen interactions is vital to rationally design HDT strategies. Here, we (i) provide an overview of key Mtb host-pathogen interactions as basis for HDT strategies; and (ii) discuss the components and utility of quantitative systems pharmacology (QSP) models to inform HDT strategies. QSP models can be used to identify and optimize treatment targets, to facilitate preclinical to human translation, and to design combination treatment strategies.

Articulating the exuberant intricacies of bacterial exopolysaccharides to purge environmental pollutants.

Microbial exopolysaccharide (EPS) is composed of a mixture of macromolecules such as proteins, polysaccharides, humic-like compounds, and nucleic acids, which encase microbial cells in a three-dimensional matrix. The literature shows that the EPS possess significant properties such as renewable, biodegradable, eco-friendly, non-toxic, and economically valued product, representing it as a green alternative to the synthetic polymer. The cost-effective and green synthesis of the EPS must be encouraged by using agro-waste as a raw material. The main objective of the manuscript is to provide a comprehensive update on the various aspects pertaining to EPS, including the economic aspects of EPS production, provide an insight into the latest tools and techniques used for detailed structural EPS characterization along with updates in the integration of CRISPR/Cas9 technology for engineering the modification in EPS production, the role of newly discovered EPR3 as a signalling molecule in plant growth-promoting properties (PGP) or agricultural microbiology. Furthermore, the EPS achieved prospective interest prevailing potential environmental issues which can be subject to EPS treatment including, landfill leachate treatment, decolourization of dye from the effluent or waste generated by an industry, removal of radionuclides, heavy metals and toxic compounds from the various environments (aquatic and terrestrial), industry effluents, waste waters etc. are comprehensively discussed.

Leveraging innovative technology to generate drug response phenotypes for the advancement of biomarker-driven precision dosing.

Although traditional approaches to biomarker discovery have elucidated key molecular markers that have improved drug selection (precision medicine), the discovery of biomarkers that inform optimal dose selection (precision dosing) continues to be a challenge in many therapeutic areas. Larger and more diverse study populations are necessary to discover additional biomarkers that provide the resolution needed for a more tailored dose. To generate and accommodate large datasets of drug response phenotypes, time- and cost-efficient strategies are necessary. In particular, a multitude of technological advances that originated for purposes outside of biomedical research (electronic health records, direct-to-consumer genetic testing, social media, mobile devices, and machine learning) have made it easier to communicate, connect, and gather information from consumers. Although these technologies have been used with success in the health sciences for an array of purposes, these resources have not been fully capitalized on for precision dosing. This perspective will touch on how these innovations can be used as data sources, data collection tools, and data processing tools for drug-response phenotypes with a unique focus on advancing biomarker-driven precision dosing.