Improving the role and contribution of pharmacokinetic analyses in antimalarial drug clinical trials.
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ABSTRACT:
SUBMITTER: Kay K
PROVIDER: S-EPMC4187976 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Malaria presents a tremendous public health burden, and new therapies are needed. Massive compound libraries screened against Plasmodium falciparum have yielded thousands of lead compounds, resulting in an acute need for rational criteria to select the best candidates for development. We reasoned that, akin to antibacterials, antimalarials might have an essential pharmacokinetic requirement for efficacy: action governed either by total exposure or peak concentration (AUC/CMAX), or by duration above a defined minimum concentration [time above minimum inhibitory concentration (TMIC)]. We devised an in vitro system for P. falciparum, capable of mimicking the dynamic fluctuations of a drug in vivo. Using this apparatus, we find that chloroquine is TMIC-dependent, whereas the efficacy of artemisinin is driven by CMAX. The latter was confirmed in a mouse model of malaria. These characteristics can explain the clinical success of two antimalarial drugs with widely different kinetics in humans. Chloroquine, which persists for weeks, is ideally suited for its TMIC mechanism, whereas great efficacy despite short exposure (t1/2 in blood 3 hours or less) is attained by CMAX-driven artemisinins. This validated preclinical model system can be used to select those antimalarial lead compounds whose CMAX or TMIC requirement for efficacy matches pharmacokinetics obtained in vivo. The apparatus can also be used to explore the kinetic dependence of other pharmacodynamic endpoints in parasites.
| S-EPMC3845824 | biostudies-literature
Project description:Drug efficacy trials monitor the continued efficacy of front-line drugs against falciparum malaria. Overestimating efficacy results in a country retaining a failing drug as first-line treatment with associated increases in morbidity and mortality, while underestimating drug effectiveness leads to removal of an effective treatment with substantial practical and economic implications. Trials are challenging: they require long durations of follow-up to detect drug failures, and patients are frequently reinfected during that period. Molecular correction based on parasite genotypes distinguishes reinfections from drug failures to ensure the accuracy of failure rate estimates. Several molecular correction "algorithms" have been proposed, but which is most accurate and/or robust remains unknown. We used pharmacological modeling to simulate parasite dynamics and genetic signals that occur in patients enrolled in malaria drug clinical trials. We compared estimates of treatment failure obtained from a selection of proposed molecular correction algorithms against the known "true" failure rate in the model. Our findings are as follows. (i) Molecular correction is essential to avoid substantial overestimates of drug failure rates. (ii) The current WHO-recommended algorithm consistently underestimates the true failure rate. (iii) Newly proposed algorithms produce more accurate failure rate estimates; the most accurate algorithm depends on the choice of drug, trial follow-up length, and transmission intensity. (iv) Long durations of patient follow-up may be counterproductive; large numbers of new infections accumulate and may be misclassified, overestimating drug failure rate. (v) Our model was highly consistent with existing in vivo data. The current WHO-recommended method for molecular correction and analysis of clinical trials should be reevaluated and updated.
| S-EPMC6709465 | biostudies-literature
Project description:Mechanism-based pharmacokinetic-pharmacodynamic (PK/PD) modelling is the standard computational technique for simulating drug treatment of infectious diseases with the potential to enhance our understanding of drug treatment outcomes, drug deployment strategies, and dosing regimens. Standard methodologies assume only a single drug is used, it acts only in its unconverted form, and that oral drugs are instantaneously absorbed across the gut wall to their site of action. For drugs with short half-lives, this absorption period accounts for a significant period of their time in the body. Treatment of infectious diseases often uses combination therapies, so we refined and substantially extended the PK/PD methodologies to incorporate (i) time lags and drug concentration profiles resulting from absorption across the gut wall and, if required, conversion to another active form; (ii) multiple drugs within a treatment combination; (iii) differing modes of action of drugs in the combination: additive, synergistic, antagonistic; (iv) drugs converted to an active metabolite with a similar mode of action. This methodology was applied to a case study of two first-line malaria treatments based on artemisinin combination therapies (ACTs, artemether-lumefantrine and artesunate-mefloquine) where the likelihood of increased artemisinin tolerance/resistance has led to speculation on their continued long-term effectiveness. We note previous estimates of artemisinin kill rate were underestimated by a factor of seven, both the unconverted and converted form of the artemisinins kill parasites and the extended PK/PD methodology produced results consistent with field observations. The simulations predict that a potentially rapid decline in ACT effectiveness is likely to occur as artemisinin resistance spreads, emphasising the importance of containing the spread of artemisinin resistance before it results in widespread drug failure. We found that PK/PD data is generally very poorly reported in the malaria literature, severely reducing its value for subsequent re-application, and we make specific recommendations to improve this situation.
| S-EPMC3715401 | biostudies-literature
Project description: Not available
| S-EPMC7613267 | biostudies-literature
Project description:Canada has a long tradition of leading practice-changing clinical trials in oncology. Here, we describe methodology, results, and interpretation of oncology RCTs with Canadian involvement compared to RCTs from other high-income countries (HICs). A literature search identified all RCTs evaluating anti-cancer therapies published 2014-2017. RCTs were classified based on the country affiliation of first authors. The study cohort included 636 HIC-led RCTs; 155 (24%) had Canadian authors. Three-quarters (112/155, 72%) of Canadian RCTs were conducted in the palliative setting, compared to two thirds (299/481, 62%) of RCTs from other HICs (p = 0.022). Canadian RCTs were more likely than those from other HICs to be supported by industry (85% vs. 69%, p < 0.001). The proportion of positive Canadian trials that met the ESMO-MCBS threshold for substantial clinical benefit was comparable to RCTs without Canadian authors (29% vs. 32%, p = 0.137). Thirteen percent (20/155) of all Canadian trials were affiliated with the Canadian Cancer Trials Group (CCTG). Canada plays a meaningful role in the global cancer research ecosystem but is overly reliant on industry support. The very low proportion of trials that identify a new treatment with substantial clinical benefit is worrisome. A renewed investment in cancer clinical trials is needed in Canada.
| S-EPMC8167552 | biostudies-literature
Project description:BackgroundTherapeutic efficacy studies in uncomplicated Plasmodium falciparum malaria are confounded by new infections, which constitute competing risk events since they can potentially preclude/pre-empt the detection of subsequent recrudescence of persistent, sub-microscopic primary infections.MethodsAntimalarial studies typically report the risk of recrudescence derived using the Kaplan-Meier (K-M) method, which considers new infections acquired during the follow-up period as censored. Cumulative Incidence Function (CIF) provides an alternative approach for handling new infections, which accounts for them as a competing risk event. The complement of the estimate derived using the K-M method (1 minus K-M), and the CIF were used to derive the risk of recrudescence at the end of the follow-up period using data from studies collated in the WorldWide Antimalarial Resistance Network data repository. Absolute differences in the failure estimates derived using these two methods were quantified. In comparative studies, the equality of two K-M curves was assessed using the log-rank test, and the equality of CIFs using Gray's k-sample test (both at 5% level of significance). Two different regression modelling strategies for recrudescence were considered: cause-specific Cox model and Fine and Gray's sub-distributional hazard model.ResultsData were available from 92 studies (233 treatment arms, 31,379 patients) conducted between 1996 and 2014. At the end of follow-up, the median absolute overestimation in the estimated risk of cumulative recrudescence by using 1 minus K-M approach was 0.04% (interquartile range (IQR): 0.00-0.27%, Range: 0.00-3.60%). The overestimation was correlated positively with the proportion of patients with recrudescence [Pearson's correlation coefficient (?): 0.38, 95% Confidence Interval (CI) 0.30-0.46] or new infection [?: 0.43; 95% CI 0.35-0.54]. In three study arms, the point estimates of failure were greater than 10% (the WHO threshold for withdrawing antimalarials) when the K-M method was used, but remained below 10% when using the CIF approach, but the 95% confidence interval included this threshold.ConclusionsThe 1 minus K-M method resulted in a marginal overestimation of recrudescence that became increasingly pronounced as antimalarial efficacy declined, particularly when the observed proportion of new infection was high. The CIF approach provides an alternative approach for derivation of failure estimates in antimalarial trials, particularly in high transmission settings.
| S-EPMC6612160 | biostudies-literature
Project description:BackgroundNew antimalarial regimens, including artemisinin-based combination therapies (ACTs), have been adopted widely as first-line treatment for uncomplicated malaria. Although these drugs appear to be safe and well-tolerated, experience with their use in Africa is limited and continued assessment of safety is a priority. However, no standardized guidelines for evaluating drug safety and tolerability in malaria studies exist. A system for monitoring adverse events in antimalarial trials conducted in Uganda was developed. Here the reporting system is described, and difficulties faced in analysing and interpreting the safety results are illustrated, using data from the trials.Case descriptionBetween 2002 and 2007, eleven randomized, controlled clinical trials were conducted to compare the efficacy, safety, and tolerability of different antimalarial regimens for treatment of uncomplicated malaria in Uganda. The approach to adverse event monitoring was similar in all studies. A total of 5,614 treatments were evaluated in 4,876 patients. Differences in baseline characteristics and patterns of adverse event reporting were noted between the sites, which limited the ability to pool and analyse data. Clinical failure following antimalarial treatment confounded associations between treatment and adverse events that were also common symptoms of malaria, particularly in areas of lower transmission intensity.Discussion and evaluationDespite prospectively evaluating for adverse events, limitations in the monitoring system were identified. New standardized guidelines for monitoring safety and tolerability in antimalarial trials are needed, which should address how to detect events of greatest importance, including serious events, those with a causal relationship to the treatment, those which impact on adherence, and events not previously reported.ConclusionAlthough the World Health Organization has supported the development of pharmacovigilance systems in African countries deploying ACTs, additional guidance on adverse events monitoring in antimalarial clinical trials is needed, similar to the standardized recommendations available for assessment of drug efficacy.
| S-EPMC2464601 | biostudies-literature
Project description:The ongoing development of new antimalarial drugs and the increasing use of controlled human malaria infection (CHMI) studies to investigate their activity in early-stage clinical trials require the development of methods to analyze their pharmacodynamic effect. This is especially so for studies where quantitative PCR (qPCR) is becoming the preferred method for assessing parasite clearance as the study endpoint. We report the development and validation of an analytic approach for qPCR-determined parasite clearance data. First, in a clinical trial with the licensed antimalarial combination sulfadoxine-pyrimethamine (S/P), qPCR data were collected from 12 subjects and used to determine qPCR replicate variability and to identify outliers. Then, an iterative analytic approach based on modeling the log-linear decay of parasitemia following drug treatment was developed to determine the parasite reduction ratio (PRR) and parasite clearance half-life, both measures of parasite clearance. This analytic approach was then validated with data from 8 subjects enrolled in a second study with the licensed antimalarial drug mefloquine. By this method, the PRR and parasite clearance half-lives for S/P and Mefloquine were determined to be 38,878 (95% confidence interval [95% CI], 17,396 to 86,889) at 3.15 (95% CI, 2.93 to 3.41) days and 157 (95% CI, 130 to 189) at 6.58 (95% CI, 6.35 to 6.83) days for the respective studies. No serious adverse events occurred in the two trials, and pharmacokinetic values were within expected ranges for sulfadoxine and pyrimethamine. The robust statistical method that we have developed to analyze qPCR-derived pharmacodynamic data from CHMI studies will facilitate the assessment of the activity of a range of experimental antimalarial drugs now entering clinical trials. (This trial was registered with the Australian New Zealand Clinical Trials Registry under registration numbers ACTRN12611001203943 and ACTRN12612000323820.).
| S-EPMC4468738 | biostudies-literature
Project description:Clinical trials monitoring malaria drug resistance require genotyping of recurrent Plasmodium falciparum parasites to distinguish between treatment failure and new infection occurring during the trial follow up period. Because trial participants usually harbour multi-clonal P. falciparum infections, deep amplicon sequencing (AmpSeq) was employed to improve sensitivity and reliability of minority clone detection. Paired samples from 32 drug trial participants were Illumina deep-sequenced for five molecular markers. Reads were analysed by custom-made software HaplotypR and trial outcomes compared to results from the previous standard genotyping method based on length-polymorphic markers. Diversity of AmpSeq markers in pre-treatment samples was comparable or higher than length-polymorphic markers. AmpSeq was highly reproducible with consistent quantification of co-infecting parasite clones within a host. Outcomes of the three best-performing markers, cpmp, cpp and ama1-D3, agreed in 26/32 (81%) of patients. Discordance between the three markers performed per sample was much lower by AmpSeq (six patients) compared to length-polymorphic markers (eleven patients). Using AmpSeq for discrimination of recrudescence and new infection in antimalarial drug trials provides highly reproducible and robust characterization of clone dynamics during trial follow-up. AmpSeq overcomes limitations inherent to length-polymorphic markers. Regulatory clinical trials of antimalarial drugs will greatly benefit from this unbiased typing method.
| S-EPMC6883076 | biostudies-literature
Project description:BackgroundDrug-drug interactions (DDIs) in subjects enrolling in clinical trials can impact not only safety of the patient but also study drug outcomes and data validity. This makes it critical to adequately screen and manage DDIs. The study objective was to determine the prevalence of DDIs involving study medications in subjects enrolling in National Clinical Trials Network (NCTN) clinical trials at a single institution. DDIs were evaluated based on study protocol recommendations for concomitant medication use (i.e. exclude, avoid or use caution), screening via DDI tool, and pharmacist review.MethodsSubjects enrolled in NCTN trials of commercially available agents between January 2013 and August 2017 were included if a complete medication list was available. Complete medication lists were collected from the date of enrollment or the next available date then screened utilizing protocol guidance and the DDI screening tool, Lexicomp® Drug Interactions (Wolters Kluwer, Hudson, OH). Interactions were reviewed for clinical relevance: defined as a DDI that would require a medication change to ensure study agent safety and efficacy at enrollment.ResultsOne hundred and twenty-eight subjects enrolled in 35 clinical trials were included. Protocol guidance detected 15 unique DDI pairs that should be avoided or used with caution in 10.2% (13/128) of subjects. The majority of these subjects did not have a clinically relevant DDI (69.2%, 9/13) based on pharmacist review. Lexicomp® detected moderate to major DDIs in 24.2% (31/128) of subjects, with 9.4% (12/128) having a clinically relevant DDI.ConclusionsThis study confirms a high prevalence of DDIs present in subjects enrolling in oncology clinical trials. Further efforts should be made to improve methods to detect and manage DDIs in patients enrolling on clinical trials to ensure patient safety and trial data validity.
| S-EPMC6249716 | biostudies-literature