***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: BACKGROUND It is believed that nonadherence is the proximate cause of multidrug-resistant tuberculosis ( MDR-tuberculosis ) emergence . The level of nonadherence associated with emergence of MDR-tuberculosis is unknown . Performance of a r and omized controlled trial in which some patients are r and omized to nonadherence would be unethical ; therefore , other study design s should be utilized . METHODS We performed hollow fiber studies for both bactericidal and sterilizing effect , with inoculum spiked with 0.5 % rifampin- and isoniazid-resistant isogenic strains in some experiments . St and ard therapy was administered daily for 28 - 56 days , with extents of nonadherence varying between 0 % and 100 % . Sizes of drug-resistant population s were compared using analysis of variance . We also explored the effect of pharmacokinetic variability on MDR-tuberculosis emergence using computer-aided clinical trial simulations of 10 000 Cape Town , South Africa , tuberculosis patients . RESULTS Therapy failure was only encountered at extents of nonadherence ≥60 % . Surprisingly , isoniazid- and rifampin-resistant population s did not achieve ≥1 % proportion in any experiment and did not achieve a higher proportion with nonadherence . However , clinical trial simulations demonstrated that approximately 1 % of tuberculosis patients with perfect adherence would still develop MDR-tuberculosis due to pharmacokinetic variability alone . CONCLUSIONS These data , based on a pre clinical model , demonstrate that nonadherence alone is not a sufficient condition for MDR-tuberculosis emergence BACKGROUND The hollow fiber system model of tuberculosis ( HFS-TB ) , in t and em with Monte Carlo experiments , represents a drug development tool ( DDT ) with the potential for use to develop tuberculosis treatment regimens . However , the predictive accuracy of the HFS-TB , or any other non clinical DDT such as an animal model , has yet to be robustly evaluated . METHODS To avoid hindsight bias , a literature search was performed to identify clinical studies published at least 6 months after HFS-TB experiments ' quantitative predictions . Steps to minimize bias and for reporting systematic review s were applied as outlined in the Preferred Reporting Items for Systematic Review s and Meta-Analyses . Publications were scored for quality of evidence . Accuracy was calculated using the mean absolute percentage error , then summated with weighting assigned by sample size and quality -of- evidence score . Given the lack of a gold-st and ard tuberculosis DDT , the forecasting accuracy of a completely unreliable tool was also calculated from 1000 simulated experiments for a r and om or " total guesswork " model . RESULTS The quantitative forecasting accuracy ( 95 % confidence interval [ CI ] ) for the " total guesswork " model was 15.6 % ( 95 % CI , 8.7%-22.5 % ) ; bias was -0.1 % ( 95 % CI , -2.5 % to 2.2 % ) . Twenty clinical studies were published after HFS-TB experiments predicted optimal drug exposures and doses , susceptibility breakpoints , and optimal combination regimens . Based on these clinical studies , the predictive accuracy of the HFS-TB was 94.4 % ( 95 % CI , 84.3%-99.9 % ) , and bias was 1.8 % ( 95 % CI , -13.7 % to 6.2 % ) . CONCLUSIONS The HFS-TB model is highly accurate at forecasting optimal drug exposures , doses , and dosing schedules for use in the clinic BACKGROUND Based on a hollow-fiber system model of tuberculosis , we hypothesize that microbiologic failure and acquired drug resistance are primarily driven by low drug concentrations that result from pharmacokinetic variability . METHODS Clinical and pharmacokinetic data were prospect ively collected from 142 tuberculosis patients in Western Cape , South Africa . Compartmental pharmacokinetic parameters of isoniazid , rifampin , and pyrazinamide were identified for each patient . Patients were then followed for up to 2 years . Classification and regression tree analysis was used to identify and rank clinical predictors of poor long-term outcome such as microbiologic failure or death , or relapse . RESULTS Drug concentrations and pharmacokinetics varied widely between patients . Poor long-term outcomes were encountered in 35 ( 25 % ) patients . The 3 top predictors of poor long-term outcome , by rank of importance , were a pyrazinamide 24-hour area under the concentration-time curve ( AUC ) ≤ 363 mg·h/L , rifampin AUC ≤ 13 mg·h/L , and isoniazid  AUC ≤ 52 mg·h/L. Poor outcomes were encountered in 32/78 patients with the AUC of at least 1 drug below the identified threshold vs 3/64 without ( odds ratio = 14.14 ; 95 % confidence interval , 4.08 - 49.08 ) . Low rifampin and isoniazid peak and AUC concentrations preceded all cases of acquired drug resistance . CONCLUSIONS Low drug AUCs are predictive of clinical outcomes in tuberculosis patients ABSTRACT The pharmacokinetics of moxifloxacin were investigated in six studies after oral administration of 50 , 100 , 200 , 400 , 600 , and 800 mg . Eight healthy male volunteers were included in each study . With doses of up to 200 mg the study was performed as a double-blind , r and omized group comparison ( n = 6 verum and n = 2 matched placebo ) ; with the higher doses the study was conducted with a double-blind , r and omized , crossover design . Safety and tolerability were assessed by evaluation of vital signs , electrocardiograms , electroencephalograms , clinical chemistry parameters , results of urinalysis , and adverse events . The drug was well tolerated . The concentrations of moxifloxacin in plasma , urine , and saliva were determined by a vali date d high-pressure liquid chromatography assay with fluorescence detection . In addition , plasma and urine sample s were analyzed by a bioassay . A good correlation between both methods was seen , indicating an absence of major active metabolites . The mean maximum concentrations of moxifloxacin in plasma ( Cmax ) ranged from 0.29 mg/liter ( 50-mg dose ) to 4.73 mg/liter ( 800-mg dose ) and were reached 0.5 to 4 h following drug administration . After reaching theCmax , plasma moxifloxacin concentrations declined in a biphasic manner . Within 4 to 5 h they fell to about 30 to 55 % of the Cmax , and thereafter a terminal half-life of 11 to 14 h accounted for the major part of the area under the concentration-time curve ( AUC ) . During the absorption phase concentrations in saliva were even higher than those in plasma , whereas in the terminal phase a constant ratio of the concentration in saliva/concentration in plasma of between 0.5 and 1 was observed , indicating a correlation between unbound concentrations in plasma and levels in saliva ( protein binding level , approximately 48 % ) . AUC and Cmax increased proportionally to the dose over the whole range of doses investigated . Urinary excretion amounted to approximately 20 % of the dose . Data on renal clearance ( 40 to 51 ml/min/1.73 m2 ) indicated partial tubular reabsorption of the drug . The pharmacokinetic parameters derived from compartmental and noncompartmental analyses were in good agreement . The kinetics could be described best by fitting the data to a two-compartment body model 
    Output:
    
The HFS-TB model offers the ability to perform PK/PD studies including humanlike drug exposures , to identify bactericidal and sterilizing effect rates , and to identify exposures associated with suppression of drug resistance .