Task Instructions and the Need for Feedback Correction Influence the Contribution of Visual Errors to Reach Adaptation.

Previous research has questioned whether motor adaptation is shaped by an optimal combination of multisensory error signals. Here, we expanded on this work by investigating how the use of visual and somatosensory error signals during online correction influences single-trial adaptation. To this end, we exposed participants to a random sequence of force-field perturbations and recorded their corrective responses as well as the after-effects exhibited during the subsequent unperturbed movement. In addition to the force perturbation, we artificially decreased or increased visual errors by multiplying hand deviations by a gain smaller or larger than one. Corrective responses to the force perturbation clearly scaled with the size of the visual error, but this scaling did not transfer one-to-one to motor adaptation and we observed no consistent interaction between limb and visual errors on adaptation. However, reducing visual errors during perturbation led to a small reduction of after-effects and this residual influence of visual feedback was eliminated when we instructed participants to control their hidden hand instead of the visual hand cursor. Taken together, our results demonstrate that task instructions and the need to correct for errors during perturbation are important factors to consider if we want to understand how the sensorimotor system uses and combines multimodal error signals to adapt movements.

Prediction of Safety Margin and Optimization of Dosing Protocol for a Novel Antibiotic using Quantitative Systems Pharmacology Modeling.

Elevations of liver enzymes have been observed in clinical trials with BAL30072, a novel antibiotic. In vitro assays have identified potential mechanisms for the observed hepatotoxicity, including electron transport chain (ETC) inhibition and reactive oxygen species (ROS) generation. DILIsym, a quantitative systems pharmacology (QSP) model of drug-induced liver injury, has been used to predict the likelihood that each mechanism explains the observed toxicity. DILIsym was also used to predict the safety margin for a novel BAL30072 dosing scheme; it was predicted to be low. DILIsym was then used to recommend potential modifications to this dosing scheme; weight-adjusted dosing and a requirement to assay plasma alanine aminotransferase (ALT) daily and stop dosing as soon as ALT increases were observed improved the predicted safety margin of BAL30072 and decreased the predicted likelihood of severe injury. This research demonstrates a potential application for QSP modeling in improving the safety profile of candidate drugs.

Monte Carlo simulations based on phase 1 studies predict target attainment of ceftobiprole in nosocomial pneumonia patients: a validation study.

Monte Carlo simulation (MCS) of antimicrobial dosage regimens during drug development to derive predicted target attainment values is frequently used to choose the optimal dose for the treatment of patients in phase 2 and 3 studies. A criticism is that pharmacokinetic (PK) parameter estimates and variability in healthy volunteers are smaller than those in patients. In this study, the initial estimates of exposure from MCS were compared with actual exposure data in patients treated with ceftobiprole in a phase 3 nosocomial-pneumonia (NP) study (NTC00210964). Results of MCS using population PK data from ceftobiprole derived from 12 healthy volunteers were used (J. W. Mouton, A. Schmitt-Hoffmann, S. Shapiro, N. Nashed, N. C. Punt, Antimicrob. Agents Chemother. 48:1713-1718, 2004). Actual individual exposures in patients were derived after building a population pharmacokinetic model and were used to calculate the individual exposure to ceftobiprole (the percentage of time the unbound concentration exceeds the MIC [percent fT > MIC]) for a range of MIC values. For the ranges of percent fT > MIC used to determine the dosage schedule in the phase 3 NP study, the MCS using data from a single phase 1 study in healthy volunteers accurately predicted the actual clinical exposure to ceftobiprole. The difference at 50% fT > MIC at an MIC of 4 mg/liter was 3.5% for PK-sampled patients. For higher values of percent fT > MIC and MICs, the MCS slightly underestimated the target attainment, probably due to extreme values in the PK profile distribution used in the simulations. The probability of target attainment based on MCS in healthy volunteers adequately predicted the actual exposures in a patient population, including severely ill patients.

Physiologically based pharmacokinetic (PBPK) modeling of disposition of epiroprim in humans.

The objective of this study was to use in synergy physiologically based and empirical approaches to estimate the drug-specific input parameters of PBPK models of disposition to simulate the plasma concentration-time profile of epiroprim in human. The estimated input parameters were the tissue:plasma partition coefficients (Pt:p) for distribution and the blood clearance (CL) for the in vivo conditions. Epiroprim represents a challenge for such methods, because it shows large interspecies differences in its pharmacokinetic properties. Two approaches were used to predict the human Pt:p values: the tissue composition model (TCM) and the "Arundel approach" based on the volume of distribution at steady state (Vdss) determined in vivo in the rat. CL in human was predicted by (1) conventional allometric scaling of in vivo animal clearances (CAS), (2) physiologically based direct scaling up of in vitro hepatocyte data (DSU), and (3) allometric scaling of animal intrinsic in vivo blood CL normalized by the ratios of animal:human intrinsic clearances determined in vitro with hepatocytes (NAS). The performance of prediction was assessed by comparing separately the above pharmacokinetic parameters (Vdss estimated from the Pt:p values and blood CL) with the corresponding in vivo data obtained from the plasma kinetic profiles. These input parameters were used in PBPK models, and the resulting plasma concentration-time profiles of epiroprim were compared with those observed in rat and human. Previously to the construction of the human PBPK model, a model for the rat was also developed to gain more confidence on the model structure and assumptions. Overall, using the TCM and the NAS for the parameterization of the distribution and clearance, respectively, the PBPK model gave the more accurate predictions of epiroprim's disposition in human. This study represents therefore an attractive approach, which may potentially help the clinical candidate selection.