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

Copper transporter protein (MctB) as a therapeutic target to elicit antimycobacterial activity against tuberculosis

Tuberculosis (TB) is a prehistoric infection and major etiologic agent of TB, Mycobacterium tuberculosis, which is considered to have advanced from an early progenitor species found in Eastern Africa. By the 1800s, there were approximately 800 to 1000 fatality case reports per 100,000 people in Europe and North America. This research suggests an In-silico study to identify potential inhibitory compounds for the target Mycobacterial copper transport protein (Mctb). ADME-based virtual screening, molecular docking, and molecular dynamics simulations were conducted to find promising compounds to modulate the function of the target protein. Four chemical compounds, namely Anti-MCT1, Anti-MCT2, Anti-MCT3 and Anti-MCT4 out of 1500 small molecules from the Diverse-lib of MTiOpenScreen were observed to completely satisfy Lipinski rule of five and Veber's rule. Further, significantly steady interactions with the MctB target protein were observed. Docking experiments have presented 9 compounds with less than -9.0 kcal/mol free binding energies and further MD simulation eventually gave 4 compounds having potential interactions and affinity with target protein and favorable binding energy ranging from -9.2 to -9.3 kcal/mol. We may propose these compounds as an effective candidate to reduce the growth of M. tuberculosis and may also assist present a novel therapeutic approach for Tuberculosis. In vivo and In vitro validation would be needed to proceed further in this direction.Communicated by Ramaswamy H. Sarma.

Combinatorial approaches for novel cardiovascular drug discovery: a review of the literature

INTRODUCTION

In this article, authors report an inclusive discussion about the combinatorial approach for the treatment of cardiovascular diseases (CVDs) and for counteracting the cardiovascular risk factors. The mentioned strategy was demonstrated to be useful for improving the efficacy of pharmacological treatments and in CVDs showed superior efficacy with respect to the classical monotherapeutic approach.

AREAS COVERED

According to this topic, authors analyzed the combinatorial treatments that are available on the market, highlighting clinical studies that demonstrated the efficacy of combinatorial drug strategies to cure CVDs and related risk factors. Furthermore, the review gives an outlook on the future perspective of this therapeutic option, highlighting novel drug targets and disease models that could help the future cardiovascular drug discovery.

EXPERT OPINION

The use of specifically designed and increasingly rational and effective drug combination therapies can therefore be considered the evolution of polypharmacy in cardiometabolic and CVDs. This approach can allow to intervene on multiple etiopathogenetic mechanisms of the disease or to act simultaneously on different pathologies/risk factors, using the combinations most suitable from a pharmacodynamic, pharmacokinetic, and toxicological perspective, thus finding the most appropriate therapeutic option.

Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Illustrated with Rifampicin

Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. In contrast, ML models are much quicker trained, but offer less mechanistic insights. The opportunity of using ML predictions of drug PK as input for a PKPD model could strongly accelerate analysis efforts. Here exemplified by rifampicin, a widely used antibiotic, we explore the ability of different ML algorithms to predict drug PK. Based on simulated data, we trained linear regressions (LASSO), Gradient Boosting Machines, XGBoost and Random Forest to predict the plasma concentration-time series and rifampicin area under the concentration-versus-time curve from 0–24 h (AUC_0–24h) after repeated dosing. XGBoost performed best for prediction of the entire PK series (R²: 0.84, root mean square error (RMSE): 6.9 mg/L, mean absolute error (MAE): 4.0 mg/L) for the scenario with the largest data size. For AUC_0–24h prediction, LASSO showed the highest performance (R²: 0.97, RMSE: 29.1 h·mg/L, MAE: 18.8 h·mg/L). Increasing the number of plasma concentrations per patient (0, 2 or 6 concentrations per occasion) improved model performance. For example, for AUC_0–24h prediction using LASSO, the R² was 0.41, 0.69 and 0.97 when using predictors only (no plasma concentrations), 2 or 6 plasma concentrations per occasion as input, respectively. Run times for the ML models ranged from 1.0 s to 8 min, while the run time for the PM model was more than 3 h. Furthermore, building a PM model is more time- and labor-intensive compared with ML. ML predictions of drug PK could thus be used as input into a PKPD model, enabling time-efficient analysis.

Pharmacometrics in tuberculosis: progress and opportunities

Tuberculosis remains one of the leading causes of death by a communicable agent, infecting up to one-quarter of the world's population, predominantly in disadvantaged communities. Pharmacometrics employs quantitative mathematical models to describe the relationships between pharmacokinetics and pharmacodynamics, and to predict drug doses, exposures, and responses. Pharmacometric approaches have provided a scientific basis for improved dosing of antituberculosis drugs and concomitantly administered antiretrovirals at the population level. The development of modelling frameworks including physiologically-based pharmacokinetics, quantitative systems pharmacology and machine learning provides an opportunity to extend the role of pharmacometrics to in silico quantification of drug-drug interactions, prediction of doses for special populations, dose optimization and individualization, and understanding the complex exposure-response relationships of multidrug regimens in terms of both efficacy and safety, informing regimen design for future study. In this short clinically-focused review, we explore what has been done, and what opportunities exist for pharmacometrics to impact tuberculosis pharmacotherapy.

Design and Analysis of Pharmacokinetics, Pharmacodynamics and Toxicological analysis of Cannabidiol analogs using in silico Tools

Background:

Background: Cannabidiol (CBD), a non‐psychoactive phytocannabinoid from Cannabis Sativa, has become an interesting option for medicinal chemists in the development of new drug candidates

Objective:

Objective: This study aims to propose analogs with therapeutic potential from the CBD scaffold. Methods: The 16 analogs proposed were designed using the PubChem Sketcher V. 2.4® software. Already, CBD analogs were subjected to different in silico tools, such as Molinspiration®; SwissADME®; SwissTargetPrediction® and OSIRIS Property Explorer

Results and Discussio:

Results and Discussion: The screening of CBD analogs carried out in this study showed compounds 9 and 16 with good affinity for interactions with CB1 and CB2 receptors. Pharmacokinetic data showed that these two compounds have good oral absorption. Finally, in silico toxicity data showed that these compounds pose no risk of a toxic event in humans

Conclusion:

Conclusion: CBD analogs 9 and 16 would have a better profile of drug candidates to be further tested in vitro and in vivo models.: CBD analogs 9 and 16 would have a better profile of drug candidates to be further tested in vitro and in vivo models.

Anti-tuberculosis Compound Screen using a Zebrafish Infection Model identifies an Aspartyl-tRNA Synthetase Inhibitor

Finding new anti-tuberculosis compounds with convincing in vivo activity is an ongoing global challenge to fight the emergence of multidrug-resistant Mycobacterium tuberculosis isolates. In this study, we exploited the medium-throughput capabilities of the zebrafish embryo infection model with Mycobacterium marinum as a surrogate for M. tuberculosis. Using a representative set of clinically established drugs, we demonstrate that this model could be predictive and selective for antibiotics that can be administered orally. We further used the zebrafish infection model to screen 240 compounds from an anti-tuberculosis hit library for their in vivo activity and identified 14 highly active compounds. One of the most active compounds was the tetracyclic compound TBA161, which was studied in more detail. Analysis of resistant mutants revealed point mutations in aspS (rv2572c), encoding an aspartyl-tRNA synthetase. The target was genetically confirmed, and molecular docking studies propose the possible binding of TBA161 in a pocket adjacent to the catalytic site. This study shows that the zebrafish infection model is suitable for rapidly identifying promising scaffolds with in vivo activity.

Summary: Exploitation of the medium-throughput capabilities of a zebrafish embryo infection model of tuberculosis to screen compounds for their in vivo activity, one of which was characterized as an aspartyl-tRNA synthetase inhibitor.

Perspectives for systems biology in the management of tuberculosis

Standardised management of tuberculosis may soon be replaced by individualised, precision medicine-guided therapies informed with knowledge provided by the field of systems biology. Systems biology is a rapidly expanding field of computational and mathematical analysis and modelling of complex biological systems that can provide insights into mechanisms underlying tuberculosis, identify novel biomarkers, and help to optimise prevention, diagnosis and treatment of disease. These advances are critically important in the context of the evolving epidemic of drug-resistant tuberculosis. Here, we review the available evidence on the role of systems biology approaches - human and mycobacterial genomics and transcriptomics, proteomics, lipidomics/metabolomics, immunophenotyping, systems pharmacology and gut microbiomes - in the management of tuberculosis including prediction of risk for disease progression, severity of mycobacterial virulence and drug resistance, adverse events, comorbidities, response to therapy and treatment outcomes. Application of the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach demonstrated that at present most of the studies provide "very low" certainty of evidence for answering clinically relevant questions. Further studies in large prospective cohorts of patients, including randomised clinical trials, are necessary to assess the applicability of the findings in tuberculosis prevention and more efficient clinical management of patients.

The Use of Translational Modelling and Simulation to Develop Immunomodulatory Therapy as an Adjunct to Antibiotic Treatment in the Context of Pneumonia

The treatment of respiratory tract infections is threatened by the emergence of bacterial resistance. Immunomodulatory drugs, which enhance airway innate immune defenses, may improve therapeutic outcome. In this concept paper, we aim to highlight the utility of pharmacometrics and Bayesian inference in the development of immunomodulatory therapeutic agents as an adjunct to antibiotics in the context of pneumonia. For this, two case studies of translational modelling and simulation frameworks are introduced for these types of drugs up to clinical use. First, we evaluate the pharmacokinetic/pharmacodynamic relationship of an experimental combination of amoxicillin and a TLR4 agonist, monophosphoryl lipid A, by developing a pharmacometric model accounting for interaction and potential translation to humans. Capitalizing on this knowledge and associating clinical trial extrapolation and statistical modelling approaches, we then investigate the TLR5 agonist flagellin. The resulting workflow combines expert and prior knowledge on the compound with the in vitro and in vivo data generated during exploratory studies in order to construct high-dimensional models considering the pharmacokinetics and pharmacodynamics of the compound. This workflow can be used to refine preclinical experiments, estimate the best doses for human studies, and create an adaptive knowledge-based design for the next phases of clinical development.

Predictive modeling to study the treatment-shortening potential of novel tuberculosis drug regimens, towards bundling of preclinical data

BACKGROUND

Given the persistently high global burden of tuberculosis (TB), effective and shorter treatment options are needed. Here, we explore the relationship between relapse and treatment length as well as inter-regimen differences for two novel anti-TB drug regimens using a mouse model of TB infection and mathematical modeling.

METHODS

Mycobacterium tuberculosis-infected mice were treated for up to 13 weeks with bedaquiline and pretomanid combined with moxifloxacin and pyrazinamide (BPaMZ) or linezolid (BPaL). Cure rates were evaluated 12 weeks after treatment completion. The standard regimen of isoniazid, rifampicin, pyrazinamide, and ethambutol (HRZE) was evaluated as a comparator.

RESULTS

Six weeks of BPaMZ was sufficient to cure all mice. In contrast, 13 weeks of BPaL and 24 weeks of HRZE did not achieve 100% cure rates. Based on mathematical model predictions, 95% probability of cure was predicted for BPaMZ, BPaL and HRZE to occur at 1.6, 4.3, and 7.9 months, respectively.

CONCLUSION

This study provides additional evidence for the treatment-shortening capacity of BPaMZ over BPaL and HRZE. To optimally utilize preclinical data for predicting clinical outcomes, and to overcome the limitations that hamper such extrapolation, we advocate bundling of available published preclinical data into mathematical models.

Anti-tuberculosis effect of isoniazid scales accurately from zebrafish to humans

Background and Purpose

There is a clear need for innovation in anti‐tuberculosis drug development. The zebrafish larva is an attractive disease model in tuberculosis research. To translate pharmacological findings to higher vertebrates, including humans, the internal exposure of drugs needs to be quantified and linked to observed response.

Experimental Approach

In zebrafish studies, drugs are usually dissolved in the external water, posing a challenge to quantify internal exposure. We developed experimental methods to quantify internal exposure, including nanoscale blood sampling, and to quantify the bacterial burden, using automated fluorescence imaging analysis, with isoniazid as the test compound. We used pharmacokinetic–pharmacodynamic modelling to quantify the exposure–response relationship responsible for the antibiotic response. To translate isoniazid response to humans, quantitative exposure–response relationships in zebrafish were linked to simulated concentration–time profiles in humans, and two quantitative translational factors on sensitivity to isoniazid and stage of infection were included.

Key Results

Blood concentration was only 20% of the external drug concentration. The bacterial burden increased exponentially, and an isoniazid dose corresponding to 15 mg·L⁻¹ internal concentration (minimum inhibitory concentration) leads to bacteriostasis of the mycobacterial infection in the zebrafish. The concentration–effect relationship was quantified, and based on that relationship and the translational factors, the isoniazid response was translated to humans, which correlated well with observed data.

Conclusions and Implications

This proof of concept study confirmed the potential of zebrafish larvae as tuberculosis disease models in translational pharmacology and contributes to innovative anti‐tuberculosis drug development, which is very clearly needed.

Structure-Based Drug Design for Tuberculosis: Challenges Still Ahead

Structure-based and computer-aided drug design approaches are commonly considered to have been successful in the fields of cancer and antiviral drug discovery but not as much for antibacterial drug development. The search for novel anti-tuberculosis agents is indeed an emblematic example of this trend. Although huge efforts, by consortiums and groups worldwide, dramatically increased the structural coverage of the Mycobacterium tuberculosis proteome, the vast majority of candidate drugs included in clinical trials during the last decade were issued from phenotypic screenings on whole mycobacterial cells. We developed here three selected case studies, i.e., the serine/threonine (Ser/Thr) kinases—protein kinase (Pkn) B and PknG, considered as very promising targets for a long time, and the DNA gyrase of M. tuberculosis, a well-known, pharmacologically validated target. We illustrated some of the challenges that rational, target-based drug discovery programs in tuberculosis (TB) still have to face, and, finally, discussed the perspectives opened by the recent, methodological developments in structural biology and integrative techniques.