Biology subjects

Van, N.

Publications and source records attributed to Van, N..

6 recordsLinked to original sources

bioRxiv2025

Antibiotic dose-response curves can measure antibiotic activity against Mycobacterium abscessus and Mycobacterium peregrinum

Mycobacterium abscessus is a drug-resistant pathogen associated with poor clinical outcomes despite treatment with multidrug antibiotic regimens. Apart from clarithromycin, antimicrobial susceptibility testing (AST) results for Mycobacterium abscessus cannot guide antibiotic selection. AST involves measuring the minimum inhibitory concentration (MIC), which is allowed to span a four-fold range in concentration. This accepted variability of the MIC limits the clinical utility of AST. Antimicrobial dose-response curves, obtained by measuring the growth inhibition of a given organism to increasing concentrations of an antibiotic, can yield metrics of antibiotic activity that are less variable than the MIC. We used Clinical and Laboratory Standards Institute growth conditions for rapid-grower nontuberculous mycobacteria to generate 990 dose-response curves across three time points (72 hours, 96 hours, and 120 hours) for six guideline-recommended (clarithromycin, amikacin, cefoxitin, linezolid, tigecycline, and clofazimine) and five new (omadacycline, tedizolid, SPR719, SQ109, and bedaquiline) antibiotics against Mycobacterium abscessus subspecies abscessus ATCC 19977 and Mycobacterium peregrinum ATCC 700686. We established the fit of the dose-response curve (R2) as a quality control metric. Using the geometric standard deviation and median coefficient of variation, we demonstrated that the IC50 and IC75 (antibiotic concentrations corresponding to 50% and 75% growth inhibition, respectively) are less variable than the MIC. We identified time-dependent changes in dose-response curve metrics that allow the detection of inducible clarithromycin resistance with only five days of incubation. This study demonstrates the potential of dose-response curves in measuring antibiotic activity against Mycobacterium abscessus.

microbiology↗

bioRxiv2024

Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action

Treatments for tuberculosis remain lengthy, motivating a search for new drugs with novel mechanisms of action. However, it remains challenging to elucidate the direct targets of a drug, and even more so, to determine which disrupted cellular processes lead to bacterial killing. We developed a computational tool, DECIPHAER (DEcoding Cross-modal Information of PHarmacologies via AutoEncodeRs), to select the important correlated transcriptional and morphological responses of Mycobacterium tuberculosis to drug treatments. By finding a reduced feature space from these measurements, DECIPHAER highlighted essential features of Mtb cellular damage such as phosphosugar stress and inhibition of translation and DNA replication. After training, DECIPHAER provides cell-death-relevant insight into single-modal datasets, enabling interrogation of drug treatment responses for which transcriptional data are unavailable. Using morphological data alone with DECIPHAER, we discovered that respiration inhibition by the poly-pharmacological drugs, SQ109 and BM212, can influence cell death more than their effects on the cell wall. This study demonstrates that DECIPHAER can extract the critical shared information from multi-modal measurements to identify cell death-relevant mechanisms of TB drugs.

microbiology↗

bioRxiv2022

Novel synergies and isolate specificities in the drug interactions landscape of Mycobacterium abscessus

Mycobacterium abscessus infections are difficult to treat and are often considered untreatable without tissue resection. Due to the intrinsic drug-resistant nature of the bacteria, combination therapy of three or more antibiotics is recommended. A major challenge in treating M. abscessus infections is the absence of a universal combination therapy with satisfying clinical success rates, leaving clinicians to treat infections using antibiotic lacking efficacy data. We systematically measured drug combinations in M. abscessus to establish a resource of drug interaction data and identify patterns of synergy to help design optimized combination therapies. We measured approximately 230 pairwise drug interactions among 22 antibiotics and identified 71 synergistic pairs, 54 antagonistic pairs, and four potentiator-antibiotics not previously reported. We found that commonly used drug combinations in the clinic, such as azithromycin and amikacin, are antagonistic in lab reference strain ATCC19977, whereas novel combinations, such as azithromycin and rifampicin, are synergistic. Another challenge in developing universally effective multidrug therapies for M. abscessus is the significant variation in drug response between isolates. We measured drug interactions in a focused set of 36 drug pairs across a small panel of clinical isolates with rough and smooth morphotypes. We observed highly strain-dependent drug interactions that cannot be predicted from single-drug susceptibility profiles or known drug mechanisms of action. Our study demonstrates the immense potential to identify synergistic drug combinations in the vast drug combination space and emphasizes the importance of strain-specific combination measurements for designing improved therapeutic interventions.

microbiology↗

bioRxiv2021

Design principles to assemble drug combinations for effective tuberculosis therapy using interpretable pairwise drug response measurements

A challenge in designing treatment regimens for tuberculosis is the necessity to use three or more antibiotics in combination. The combination space is too large to be comprehensively assayed; therefore, only a small number of possible combinations are tested. We narrowed the prohibitively large search space of combination drug responses by breaking down high-order combinations into units of drug pairs. Using pairwise drug potency and drug interaction metrics from in vitro experiments across multiple growth environments, we trained machine learning models to predict outcomes associated with higher-order combinations in the BALB/c relapsing mouse model, an important preclinical model for drug development. We systematically predicted treatment outcomes of >500 combinations among twelve antibiotics. Our classifiers performed well on test data and predicted many novel combinations to be improved over bedaquiline + pretomanid + linezolid, an effective regimen for multidrug-resistant tuberculosis that also shortens treatment in BALB/c mice compared to the standard of care. To understand the design features of effective drug combinations, we reformulated classifiers as simple rulesets to reveal guiding principles of constructing combination therapies for both preclinical and clinical outcomes. One example ruleset is to include a drug pair that is synergistic in dormancy and another pair that is potent in a cholesterol-rich growth environment. These rulesets are predictive, intuitive, and practical, thus enabling rational construction of effective drug combinations based on in vitro pairwise drug synergies and potencies. As more preclinical and clinical drug combination data become available, we expect to improve predictions and combination design rules.

microbiology↗

bioRxiv2021

C25-modified rifamycin derivatives with improved activity against Mycobacterium abscessus

Infections caused by Mycobacterium abscessus are difficult to treat due to its intrinsic resistance to most antibiotics. Formation of biofilms and the capacity of M. abscessus to survive inside host phagocytes further complicate eradication. Herein, we explored whether addition of a carbamate-linked group at the C25 position of rifamycin SV blocks enzymatic inactivation by ArrMab, an ADP-ribosyltransferase conferring resistance to rifampicin. Unlike rifampicin, 5j, a benzyl piperidine rifamycin derivative with a morpholino substituted C3 position, is not modified by purified ArrMab. Additionally, we show that the ArrMab D82 residue is essential for catalytic activity. Thermal profiling of ArrMab in the presence of 5j, rifampicin or rifabutin shows that 5j does not bind to ArrMab. We found that the activity of 5j is comparable to amikacin against M. abscessus planktonic cultures and pellicles. Critically, 5j also exerts potent antimicrobial activity against M. abscessus in human macrophages and shows synergistic activity with amikacin and azithromycin.

microbiology↗

bioRxiv2021

Systematic measurement of combination drug landscapes to predict in vivo treatment outcomes for tuberculosis

A lengthy multidrug chemotherapy is required to achieve a durable cure in tuberculosis. Variation in Mycobacterium tuberculosis drug response is created by the differing microenvironments in lesions, which generate different bacterial drug susceptibilities. To better realize the potential of combination therapy to shorten treatment duration, multidrug therapy design should deliberately explore the vast combination space. We face a significant scaling challenge in making systematic drug combination measurements because it is not practical to use animal models for comprehensive drug combination studies, nor are there well-validated high-throughput in vitro models that predict animal outcomes. We hypothesized that we could both prioritize combination therapies and quantify the predictive power of various in vitro models for drug development using a dataset of drug combination dose responses measured in multiple in vitro models. We systematically measured M. tuberculosis response to all 2- and 3-drug combinations among ten antibiotics in eight conditions that reproduce lesion microenvironments. Applying machine learning to this comprehensive dataset, we developed classifiers predictive of multidrug treatment outcome in a mouse model of disease relapse. We trained classifiers on multiple mouse models and identified ensembles of in vitro models that best describe in vivo treatment outcomes. Furthermore, we found that combination synergies are less important for predicting outcome than metrics of potency. Here, we map a path forward to rationally prioritize combinations for animal and clinical studies using systematic drug combination measurements with validated in vitro models. Our pipeline is generalizable to other difficult-to-treat diseases requiring combination therapies. One Sentence SummarySignatures of in vitro potency and drug interaction measurements predict combination therapy outcomes in mouse models of tuberculosis.

microbiology↗