References
- Pienaar E, Sarathy J, Prideaux B, et al. Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach. PLoS Comput Biol. 2017;13(8):e1005650.
- Dheda K, Lenders L, Magombedze G, et al. Drug penetration gradients associated with acquired drug resistance in tuberculosis patients. Am J Respir Crit Care Med. 2018;198(9):1208–1219.
- Chen C, Wicha SG, Nordgren R, et al. Comparisons of analysis methods for assessment of pharmacodynamic interactions including design recommendations. Aaps J. 2018;20:77.
- Pasipanodya JG, Nuermberger E, Romero K, et al. Systematic analysis of hollow fiber model of tuberculosis experiments. Clin Infect Dis. 2015;61(suppl 1):S10–7.
- Lenaerts A, Barry CE, Dartois V, et al. Heterogeneity in tuberculosis pathology, microenvironments and therapeutic responses. Immunol Rev. 2015;264(1):288–307.
- Gumbo T, Louie A, Deziel MR, et al. Selection of a moxifloxacin dose that suppresses drug resistance in mycobacterium tuberculosis, by use of an in vitro pharmacodynamic infection model and mathematical modeling. J Infect Dis. 2004;190(9):1642–1651.
- Gumbo T, Louie A, Deziel MR, et al. Concentration-dependent mycobacterium tuberculosis killing and prevention of resistance by rifampin. Antimicrob Agents Chemother. 2007;51(11):3781–3788.
- Gumbo T, Pasipanodya JG, Romero K, et al. Forecasting accuracy of the hollow fiber model of tuberculosis for clinical therapeutic outcomes. Clin Infect Dis. 2015;61(suppl 1):S25–31.
- Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018;15(4):233–234.
- Rockwood N, Pasipanodya JG, Denti P, et al. Concentration-dependent antagonism and culture conversion in pulmonary tuberculosis. Clin Infect Dis. 2017;64(10):1350–1359.
- Pasipanodya JG, Smythe W, Merle CS, et al. Artificial intelligence–derived 3-way concentration-dependent antagonism of gatifloxacin, pyrazinamide, and rifampicin during treatment of pulmonary tuberculosis. Clin Infect Dis. 2018;67(suppl_3):S284–92.
- Swaminathan S, Pasipanodya JG, Ramachandran G, et al. Drug concentration thresholds predictive of therapy failure and death in children with tuberculosis: bread crumb trails in random forests. Clin Infect Dis. 2016;63(suppl 3):S63–74.
- Chigutsa E, Pasipanodya JG, Visser ME, et al. Impact of nonlinear interactions of pharmacokinetics and mics on sputum bacillary kill rates as a marker of sterilizing effect in tuberculosis. Antimicrob Agents Chemother. 2015;59(1):38–45.
- Deshpande D, Pasipanodya JG, Mpagama SG, et al. Ethionamide pharmacokinetics/pharmacodynamics derived dose, the role of mics in clinical outcome, and the resistance arrow of time in multidrug-resistant tuberculosis. Clin Infect Dis. 2018;67(suppl_3):S317–26.
- Deshpande D, Pasipanodya JG, Mpagama SG, et al. Levofloxacin pharmacokinetics/pharmacodynamics, dosing, susceptibility breakpoints, and artificial intelligence in the treatment of multidrug-resistant tuberculosis. Clin Infect Dis. 2018;67(suppl_3):S293–302.
- Rokach L, Maimon O. Data mining with decision trees: theory and applications. 2nd ed. River Edge, NJ: World Scientific Publishing Co., Inc; 2014.
- Chideya S, Winston CA, Peloquin CA, et al. Isoniazid, rifampin, ethambutol, and pyrazinamide pharmacokinetics and treatment outcomes among a predominantly hiv-infected cohort of adults with tuberculosis from Botswana. Clin Infect Dis. 2009;48(12):1685–1694.
- Pasipanodya JG, McIlleron H, Burger A, et al. Serum drug concentrations predictive of pulmonary tuberculosis outcomes. J Infect Dis. 2013;208(9):1464–1473.
- Breiman L, Friedman JH, Olshen RA, et al. Classification and regression trees. New York: Chapman & Hall; 1984.
- World Health Organization. Treatment of tuberculosis: guidelines. 4th ed. Geneva, Switzerland: WHO/HTM/TB/2009.420. World Health Organization; 2010.
- Park JS, Lee JY, Lee YJ, et al. Serum levels of antituberculosis drugs and their effect on tuberculosis treatment outcome. Antimicrob Agents Chemother. 2016;60(1):92–98.
- Modongo C, Pasipanodya JG, Magazi BT, et al. Artificial intelligence and amikacin exposures predictive of outcomes in multidrug-resistant tuberculosis patients. Antimicrob Agents Chemother. 2016;60(10):5928–5932.
- Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York: Springer; 2009.
- Hothorn T, Hornik K, Zeileis A. Unbiased recursive partitioning: A conditional inference framework. J Comput Graph Stat 2006;15(3):651–674.
- Breiman L Arcing the edge. Technical report 486. Berkeley: Statistics Department, University of California; 1997.
- Friedman JH. Greedy function approximation: A gradient boosting machine. Ann Stat. 2001;29(5):1189–1232.
- Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.
- Friedman JH. Multivariate adaptive regression splines. Ann Stat. 1991;19(1):1–67.
- Clewe O, Aulin L, Hu Y, et al. A multistate tuberculosis pharmacometric model: a framework for studying anti-tubercular drug effects in vitro. J Antimicrob Chemother. 2016;71(4):964–974.
- Svensson RJ, Simonsson USH. Application of the multistate tuberculosis pharmacometric model in patients with rifampicin-treated pulmonary tuberculosis. CPT Pharmacometrics Syst Pharmacol. 2016;5(5):264–273.
- Magombedze G, Pasipanodya JG, Srivastava S, et al. Transformation morphisms and time-to-extinction analysis that map therapy duration from preclinical models to patients with tuberculosis: translating from apples to oranges. Clin Infect Dis. 2018;67(suppl_3):S349–58.
- Gumbo T, Dona CS, Meek C, et al. Pharmacokinetics-pharmacodynamics of pyrazinamide in a novel in vitro model of tuberculosis for sterilizing effect: a paradigm for faster assessment of new antituberculosis drugs. Antimicrob Agents Chemother. 2009;53(8):3197–3204.
- Bartelink IH, Zhang N, Keizer RJ, et al. New paradigm for translational modeling to predict long-term tuberculosis treatment response. Clin Transl Sci. 2017;10(5):366–379.
- Guiastrennec B, Ramachandran G, Karlsson MO, et al. Suboptimal antituberculosis drug concentrations and outcomes in small and hiv-coinfected children in india: recommendations for dose modifications. Clin Pharmacol Ther. 2018;104(4):733–741.
- Gumbo T, Alffenaar J-WC. Pharmacokinetic/Pharmacodynamic background and methods and scientific evidence base for dosing of second-line tuberculosis drugs. Clin Infect Dis. 2018;67(suppl_3):S267–73.
- Gumbo T, Angulo-Barturen I, Ferrer-Bazaga S. Pharmacokinetic-pharmacodynamic and dose-response relationships of antituberculosis drugs: recommendations and standards for industry and academia. J Infect Dis. 2015;211(suppl 3):S96–106.
- Gumbo T, Louie A, Liu W, et al. Isoniazid bactericidal activity and resistance emergence: integrating pharmacodynamics and pharmacogenomics to predict efficacy in different ethnic populations. Antimicrob Agents Chemother. 2007;51(7):2329–2336.
- Velásquez GE, Brooks MB, Coit JM, et al. Efficacy and safety of high-dose rifampin in pulmonary tuberculosis. A randomized controlled trial. Am J Respir Crit Care Med. 2018;198(5):657–666.
- Boeree MJ, Diacon AH, Dawson R, et al. A dose-ranging trial to optimize the dose of rifampin in the treatment of tuberculosis. Am J Respir Crit Care Med. 2015;191(9):1058–1065.
- Svensson EM, Svensson RJ, Te Brake LHM, et al. The potential for treatment shortening with higher rifampicin doses: relating drug exposure to treatment response in patients with pulmonary tuberculosis. Clin Infect Dis. 2018;67(1):34–41.
- Svensson EM, Karlsson MO. Modelling of mycobacterial load reveals bedaquiline’s exposure-response relationship in patients with drug-resistant TB. J Antimicrob Chemother. 2017;72(12):3398–3405.
- Chigutsa E, Patel K, Denti P, et al. A time-to-event pharmacodynamic model describing treatment response in patients with pulmonary tuberculosis using days to positivity in automated liquid mycobacterial culture. Antimicrob Agents Chemother. 2013;57(2):789–795.
- Hajjem A, Larocque D, Bellavance F. Generalized mixed effects regression trees. Stat Probab Lett. 2017;126:114–118.
- Aljayyoussi G, Jenkins VA, Sharma R, et al. Pharmacokinetic-Pharmacodynamic modelling of intracellular Mycobacterium tuberculosis growth and kill rates is predictive of clinical treatment duration. Sci Rep. 2017;7(1):502.
- Strydom N, Gupta SV, Fox WS, et al. Tuberculosis drugs’ distribution and emergence of resistance in patient’s lung lesions: A mechanistic model and tool for regimen and dose optimization. Murray M, editor. PLOS Med. 2019;16(4):e1002773.
- Cordes H, Thiel C, Aschmann HE, et al. A physiologically based pharmacokinetic model of isoniazid and its application in individualizing tuberculosis chemotherapy. Antimicrob Agents Chemother. 2016;60(10):6134–6145.