POPULATION PHARMACOKINETICS OF CONTINUOUS INFUSION OF FACTOR VIII IN HEMOPHILIA-A PATIENTS UNDERGOING ORTHOPEDIC SURGERY

Objective: to develop a population pharmacokinetic model taking into account blood losses during and after orthopedic surgery in adult hemophilia A patients receiving infusion of coagulation factor VIII, and to evaluate the influence of potential covariates.Methods: Factor VIII pharmacokinetic parameters were calculated from 24 patients. Among them, 7 were HIV+. The observations were analyzed with the mixed-effects compartment pharmacokinetic package NONMEM and the first-order conditional estimation method. To evaluate the stability and robustness of the final model the bootstrap method was used.Results: During the model-building process, central volume of distribution (V1) was related to body weight (P = 0.0263) and viral status (P = 0.0078). Moreover, the peripheral volume of distribution was related to body weight (P=0.0362). In the final model, only the viral status was significant for V1 when compared with the base model. Posterior predictive checks and robustness analysis showed that the model adequately described the pharmacokinetic parameters. The HIV covariate accounted for 29.8% of the unexplained variation across patients for V1. V1 increased by 33.3% in HIV+ patients compared to HIV- patients.Conclusion: A population pharmacokinetic model taking into account blood losses during and after orthopedic surgery was developed. The 33.3% increase in V1 observed in HIV+ patients explained the need for higher doses in these patients.Â

Novel study designs to investigate the placebo response

<p>Abstract</p> <p>Background</p> <p>Investigating the size and mechanisms of the placebo response in clinical trials have relied on experimental procedures that simulate the double-blind randomized placebo-controlled design. However, as the conventional design is thought to elucidate drug rather than placebo actions, different methodological procedures are needed for the placebo response.</p> <p>Methods</p> <p>We reviewed the respective literature for trials designs that may be used to elucidate the size of the placebo response and the mechanisms associated with it.</p> <p>Results</p> <p>In general, this can be done by either manipulation the information provided to the subjects, or by manipulation the timing of the drug applied. Two examples of each strategy are discussed: the "balanced placebo design" (BDP) and the "balanced cross-over design" (BCD) and their variants are based on false information, while the "hidden treatment" (HT) and the ""delayed response test" (DRT) are based on manipulating the time of drug action. Since most such approaches include deception or incomplete information of the subjects they are suitable for patient only with authorized deception.</p> <p>Conclusion</p> <p>Both manipulating the information provided to subjects (BDP, DCD) or manipulating the timing of drug application (HT, DRT) allows overcoming some of the restrictions of conventional drug trials in the assessment of the placebo response, but they are feasible mostly in healthy subjects for ethical reasons.</p

Modeling of prolactin response following dopamine Dreceptor antagonists in rats:can it be translated to clinical dosing?

Prolactin release is a side effect of antipsychotic therapy with dopamine antagonists, observed in rats as well as humans. We examined whether two semimechanistic models could describe prolactin response in rats and subsequently be translated to predict pituitary dopamine D2receptor occupancy and plasma prolactin concentrations in humans following administration of paliperidone or remoxipride. Data on male Wistar rats receiving single or multiple doses of risperidone, paliperidone, or remoxipride was described by two semimechanistic models, the precursor pool model and the agonist-antagonist interaction model. Using interspecies scaling approaches, human D2receptor occupancy and plasma prolactin concentrations were predicted for a range of clinical paliperidone and remoxipride doses. The predictions were compared with corresponding observations described in literature as well as with predictions from published models developed on human data. The pool model could predict D2receptor occupancy and prolactin response in humans following single doses of paliperidone and remoxipride. Tolerance of prolactin release was predicted following multiple doses. The interaction model underpredicted both D2receptor occupancy and prolactin response. Prolactin elevation may be deployed as a suitable biomarker for interspecies translation and can inform the clinical safe and effective dose range of antipsychotic drugs. While the pool model was more predictive than the interaction model, it overpredicted tolerance on multiple dosing. Shortcomings of the translations reflect the need for better mechanistic models

Biomarkers in motor neuron disease: A state of the art review

Motor neuron disease can be viewed as an umbrella term describing a heterogeneous group of conditions, all of which are relentlessly progressive and ultimately fatal. The average life expectancy is 2 years, but with a broad range of months to decades. Biomarker research deepens disease understanding through exploration of pathophysiological mechanisms which, in turn, highlights targets for novel therapies. It also allows differentiation of the disease population into sub-groups, which serves two general purposes: (a) provides clinicians with information to better guide their patients in terms of disease progression, and (b) guides clinical trial design so that an intervention may be shown to be effective if population variation is controlled for. Biomarkers also have the potential to provide monitoring during clinical trials to ensure target engagement. This review highlights biomarkers that have emerged from the fields of systemic measurements including biochemistry (blood, cerebrospinal fluid, and urine analysis); imaging and electrophysiology, and gives examples of how a combinatorial approach may yield the best results. We emphasize the importance of systematic sample collection and analysis, and the need to correlate biomarker findings with detailed phenotype and genotype data

Artificial intelligence approach for the analysis of placebo-controlled clinical trials in major depressive disorders accounting for individual propensity to respond to placebo

Abstract Treatment effect in clinical trials for major depressive disorders (RCT) can be viewed as the resultant of treatment specific and non-specific effects. Baseline individual propensity to respond non-specifically to any treatment or intervention can be considered as a major non-specific confounding effect. The greater is the baseline propensity, the lower will be the chance to detect any treatment-specific effect. The statistical methodologies currently applied for analyzing RCTs doesn’t account for potential unbalance in the allocation of subjects to treatment arms due to heterogenous distributions of propensity. Hence, the groups to be compared may be imbalanced, and thus incomparable. Propensity weighting methodology was used to reduce baseline imbalances between arms. A randomized, double-blind, placebo controlled, three arms, parallel group, 8-week, fixed-dose study to evaluate efficacy of paroxetine CR 12.5 and 25 mg/day is presented as a cases study. An artificial intelligence model was developed to predict placebo response at week 8 in subjects assigned to placebo arm using changes from screening to baseline of individual Hamilton Depression Rating Scale items. This model was used to predict the probability to respond to placebo in each subject. The inverse of the probability was used as weight in the mixed-effects model applied to assess treatment effect. The analysis with and without propensity weight indicated that the weighted analysis provided an estimate of treatment effect and effect-size about twice larger than the non-weighted analysis. Propensity weighting provides an unbiased strategy to account for heterogeneous and uncontrolled placebo effect making patients’ data comparable across treatment arms