2020
Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones
Clarelli F, Palmer A, Singh B, Storflor M, Lauksund S, Cohen T, Abel S, Wiesch P. Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones. PLOS Computational Biology 2020, 16: e1008106. PMID: 32797079, PMCID: PMC7449454, DOI: 10.1371/journal.pcbi.1008106.Peer-Reviewed Original ResearchConceptsDose levelsAntibiotic efficacyDrug-target bindingDose-response relationshipBeta-lactams ampicillinTime-kill curvesDose-response curveNovel resistance mutationsMeasurable biochemical parametersClinical isolatesAntibiotic dose-response curvesAntibiotic actionResistance mutationsBiochemical parameters
2018
Towards better prediction of Mycobacterium tuberculosis lineages from MIRU-VNTR data
Thain N, Le C, Crossa A, Ahuja SD, Meissner JS, Mathema B, Kreiswirth B, Kurepina N, Cohen T, Chindelevitch L. Towards better prediction of Mycobacterium tuberculosis lineages from MIRU-VNTR data. Infection Genetics And Evolution 2018, 72: 59-66. PMID: 29960078, PMCID: PMC6708508, DOI: 10.1016/j.meegid.2018.06.029.Peer-Reviewed Original Research
2017
Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies
Wiesch P, Clarelli F, Cohen T. Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies. PLOS Computational Biology 2017, 13: e1005321. PMID: 28060813, PMCID: PMC5257006, DOI: 10.1371/journal.pcbi.1005321.Peer-Reviewed Original ResearchConceptsOptimal dosingTreatment strategiesOnset of actionAntibiotic treatment strategiesAntibiotic concentrationsOptimal therapyFrequent dosingHigh dosesBacterial infectionsDrug concentrationsDosingBacterial replicationAntibioticsPhysiological fluctuationsAntibiotic effectCellsHIVBacterial growthTherapyCancerInfectionMalariaDoses
2016
ClassTR: Classifying Within-Host Heterogeneity Based on Tandem Repeats with Application to Mycobacterium tuberculosis Infections
Chindelevitch L, Colijn C, Moodley P, Wilson D, Cohen T. ClassTR: Classifying Within-Host Heterogeneity Based on Tandem Repeats with Application to Mycobacterium tuberculosis Infections. PLOS Computational Biology 2016, 12: e1004475. PMID: 26829497, PMCID: PMC4734664, DOI: 10.1371/journal.pcbi.1004475.Peer-Reviewed Original Research
2013
Bayesian Estimation of Mixture Models with Prespecified Elements to Compare Drug Resistance in Treatment-Naïve and Experienced Tuberculosis Cases
Izu A, Cohen T, DeGruttola V. Bayesian Estimation of Mixture Models with Prespecified Elements to Compare Drug Resistance in Treatment-Naïve and Experienced Tuberculosis Cases. PLOS Computational Biology 2013, 9: e1002973. PMID: 23555210, PMCID: PMC3605089, DOI: 10.1371/journal.pcbi.1002973.Peer-Reviewed Original ResearchConceptsTreatment-experienced patientsDrug-resistant strainsMultiple drug-resistant strainsTreatment-naïve patientsDrug-resistant tuberculosisMycobacterium tuberculosis isolatesWorld Health OrganizationDrug resistance pathwaysTreatment-naïveTuberculosis casesTuberculosis isolatesWorldwide surveillanceDrug resistancePatientsHealth OrganizationResistant strainsResistant pathogensResistance pathwaysLow transmissibilityPathwayTuberculosis