Data Integrity in Global Clinical Trials: Discussions From Joint US Food and Drug Administration and UK Medicines and Healthcare Products Regulatory Agency Good Clinical Practice Workshop.

Good Clinical Practice (GCP) is an international ethical and scientific quality standard for designing, conducting, recording, and reporting clinical trials. Regulatory agencies conduct GCP inspections to verify the integrity of data generated in clinical trials and to assure the protection of human research subjects, in addition to ensuring that clinical trials are conducted according to the applicable regulations. The first joint GCP workshop of the US Food and Drug Administration (FDA) Center for Drug Evaluation and Research (CDER) and the United Kingdom Medicines and Healthcare products Regulatory Agency (MHRA-UK) was held in October 2018 and provided the agencies' perspectives on the importance of data quality management practices on data integrity. Regulatory perspectives on data blinding to minimize introduction of bias, and the role of audit trails in assessing data integrity in global clinical trials were discussed. This paper summarizes considerations of both agencies on these topics, along with case examples.

Assessment of differences in antimicrobial effect determined with two in vitro pharmacodynamic models: impact of surface area to volume ratio.

STUDY OBJECTIVE: To measure the influence of different surface area:volume ratios (SA:Vs) on antibiotic penetration and subsequent antibacterial effect. DESIGN: In vitro laboratory experiment. SETTING: Two academic research laboratories. INTERVENTION: The two models with effective SA:Vs of 5.34 and 4.80 cm(-1) were evaluated by conducting a time-kill experiment with Pseudomonas aeruginosa ATCC 27853 and ceftazidime. MEASUREMENTS AND MAIN RESULTS: Ceftazidime was administered by constant infusion into the central compartment. Its penetration into the peripheral compartment and bacterial counts were determined over 24 hours, and antibacterial effect was quantified. Antibiotic penetration, calculated using central compartment and peripheral compartment area under the concentration-time curves, and effect, quantified as the relationship between the areas under growth and kill curves, differed between the models. Antibiotic penetration into the peripheral compartment was 53% greater over the first 4 hours of the experiment in the model with the larger SA:V. This was associated with antibacterial effects that were 64% and 38% greater in the 0-4-hour and 0-24-hour time periods, respectively. CONCLUSION: Differences in antibiotic penetration and effect observed between these models are likely explained by differences in SA:V

Determination of antibiotic effect in an in vitro pharmacodynamic model: comparison with an established animal model of infection.

Animal infection models have historically been used to study pharmacodynamic relationships. Similar results could theoretically be produced by using an in vitro pharmacodynamic model as an alternative to animal models. We compared the antibiotic effects of ticarcillin administered in various doses and dosing regimens against Pseudomonas aeruginosa ATCC 27853 under conditions analogous to those previously employed in a neutropenic-mouse thigh infection model (B. Vogelman et al., J. Infect. Dis. 158:831-847, 1988). Ticarcillin dosages of either 96, 192, or 384 mg/day were administered at 1-, 2-, 3-, 4-, 8-, 12-, or 24-h intervals into a two-compartment model in order to duplicate the concentration-time profiles of the animal model. Colony counts were enumerated at 0 and 24 h. Linear regression and sigmoidal maximum-effect (Emax) model fitting were used to assess the relationship between the percentage of time that the concentration remained above the MIC (%T>MIC) or above four times the MIC (%T>4xMIC) and the change in the log(10) CFU per milliliter (Deltalog(10) CFU/ml) in the central and peripheral compartments. Statistical analysis of the Deltalog(10) CFU/ml values was performed for matched regimens of the in vitro and animal models based on the %T>MICs. The slopes of the regression equations of %T>MICs relative to Deltalog(10) CFU/ml values were similar for the in vitro and animal models, but the y intercept was greater with the in vitro model. The Deltalog(10) CFU/ml values of the 0- to 24-h colony counts at equivalent %T>MICs in the two models were not statistically different (P = 0.087). Overall, the peripheral compartment of the in vitro model was a better predictor of effect than the central compartment. This study, which compares pharmacodynamic principles between an in vitro and an animal model, demonstrated similar relationships between %T>MICs and effects.

Differences in antimicrobial drug exposure in patients with various degrees of renal function based on recommendations from dosing references.

STUDY OBJECTIVE: To calculate and compare 24-hour area under the unbound drug concentration-time curves (AUC(0-24)) of antimicrobials with dosing recommendations from six commonly used dosing references. INTERVENTION: Unbound plasma concentration-time profiles of 13 antimicrobial agents (4 penicillins, 3 cephalosporins, 2 carbapenems, aztreonam, 3 fluoroquinolones) were simulated at steady state using a one-compartment open model for a 70-kg adult based on pharmacokinetic parameters obtained from peer-reviewed literature. Simulations were performed at five levels of creatinine clearance (Cl(cr)). MEASUREMENTS AND MAIN RESULTS: Differences in AUC(0-24) for each antimicrobial agent were noted among the six references at each level of Cl(cr) as well as within references across the range of Cl(cr). In addition, up to 16-fold and 3-fold ranges in AUC(0-24) values were observed for beta-lactams and fluoroquinolones, respectively, in one reference based on dosing recommendations at a single level of Cl(cr) (due to more than one dose and/or dosing interval). CONCLUSION: Clinicians should be aware of differences among common references when selecting dosages of antimicrobial agents, especially for patients with moderate-to-severe renal impairment.

Comparison of methods of interpretation of checkerboard synergy testing.

Four different methods for interpreting the results of checkerboard synergy testing were compared by applying each to a set of synergy study data. Statistically significant differences in synergy were detected among methods (% synergy ranged from 10 to 83%). As interpretations were found to vary widely based upon method, one should be aware of this in interpreting the relevant literature.