Opportunities and pitfalls in clinical proof-of-concept: principles and examples

Low doses of fluoxetine for the treatment of emotional premenstrual syndrome: a randomized double-blind, placebo-controlled, pilot study

INTRODUCTION

The neuroactive metabolite of progesterone, allopregnanolone (ALLO), has been implicated in premenstrual syndrome (PMS) physiopathology and preclinical studies suggested that low doses of fluoxetine increase the ALLO brain concentration.

OBJECTIVES

To assess which low dose of fluoxetine (2 mg/d, 5 mg/d or 10 mg/d), administered exclusively during the luteal phase of menstrual cycle, has a potential effect for preventing or mitigating emotional PMS symptoms.

METHODS

In this randomized, double-blind, placebo-controlled pilot study, we followed 40 women (mean age = 29.7 +/- 7.4 years) with emotional PMS, during two menstrual cycles: cycle 1, without pharmacological intervention; and cycle 2, with pharmacological intervention. Participants took capsules, on average, seven days preceding the likely date of menses. We assessed the severity of PMS symptoms in both cycles using the Daily Record of Severity of Problems scale (DRSP).

RESULTS

There was an increase in the DRSP scores during the late luteal phase of cycle 1, confirming the diagnosis of emotional PMS. Low doses of fluoxetine (5 mg/d: 33.5%; 10 mg/d: 48.4%) reduced DRSP total score in the day before menses (day-1) at cycle 2 compared with day-1 at cycle 1. Fluoxetine 10 mg/d had the most consistent decline in emotional PMS symptoms; 70% of the participants reported a reduction greater than 40% in the DRSP score.

CONCLUSIONS

Low doses of fluoxetine, which may have no or few effect on the serotonergic system, but may interfere in the progesterone metabolization, seem to have some potential to mitigate emotional PMS symptoms. While the 10 mg/d of fluoxetine had the best performance on reducing emotional PMS symptoms, the 5 mg/d dose also seems to have some effect on emotional PMS symptoms. Further larger studies will help establish the lowest effective dose of flouxetine for PMS treatment.

Concept and application of the probability of pharmacological success (PoPS) as a decision tool in drug development: a position paper

BACKGROUND

In drug development, few molecules from a large pool of early candidates become successful medicines after demonstrating a favourable benefit-risk ratio. Many decisions are made along the way to continue or stop the development of a molecule. The probability of pharmacological success, or PoPS, is a tool for informing early-stage decisions based on benefit and risk data available at the time.

RESULTS

The PoPS is the probability that most patients can achieve adequate pharmacology for the intended indication while minimising the number of subjects exposed to safety risk. This probability is usually a function of dose; hence its computation typically requires exposure-response models for pharmacology and safety. The levels of adequate pharmacology and acceptable risk must be specified. The uncertainties in these levels, in the exposure-response relationships, and in relevant translation all need to be identified. Several examples of different indications are used to illustrate how this approach can facilitate molecule progression decisions for preclinical and early clinical development. The examples show that PoPS assessment is an effective mechanism for integrating multi-source data, identifying knowledge gaps, and forcing transparency of assumptions. With its application, translational modelling becomes more meaningful and dose prediction more rigorous. Its successful implementation calls for early planning, sound understanding of the disease-drug system, and cross-discipline collaboration. Furthermore, the PoPS evolves as relevant knowledge grows.

CONCLUSION

The PoPS is a powerful evidence-based framework to formally capture multiple uncertainties into a single probability term for assessing benefit-risk ratio. In GSK, it is now expected for governance review at all early-phase decision gates.

A framework to guide dose & regimen strategy for clinical drug development

Optimizing new drug therapies remains a challenge for clinical development, despite the use of ever more sophisticated quantitative methodologies. Although conceptually simple, the idea of finding the right treatment at the right dose for the right patient to ensure an appropriate balance of risks and benefits is challenging and requires a multidisciplinary approach. In this paper, we present a framework developed as a tool for organizing knowledge and facilitating collaboration in development teams.

Detecting placebo and drug effects on Parkinson's disease symptoms by longitudinal item-score models

This study tested the hypothesis that analyzing longitudinal item scores of the Unified Parkinson's Disease Rating Scale could allow a smaller trial size and describe a drug's effect on symptom progression. Two historical studies of the dopaminergic drug ropinirole were analyzed: a cross-over formulation comparison trial in 161 patients with early-stage Parkinson's disease, and a 24-week, parallel-group, placebo-controlled efficacy trial in 393 patients with advanced-stage Parkinson's disease. We applied item response theory to estimate the patients' symptom severity and developed a longitudinal model using the symptom severity to describe the time course of the placebo response and the drug effect on the time course. Similarly, we developed a longitudinal model using the total score. We then compared sample size needs for drug effect detection using these two different models. Total score modeling estimated median changes from baseline at 24 weeks (90% confidence interval) of -3.7 (-5.4 to -2.0) and -9.3 (-11 to -7.3) points by placebo and ropinirole. Comparable changes were estimated (with slightly higher precision) by item-score modeling as -2.0 (-4.0 to -1.0) and -9.0 (-11 to -8.0) points. The treatment duration was insufficient to estimate the symptom progression rate; hence the drug effect on the progression could not be assessed. The trial sizes to detect a drug effect with 80% power on total score and on symptom severity were estimated (at the type I error level of 0.05) as 88 and 58, respectively. Longitudinal item response analysis could markedly reduce sample size; it also has the potential for assessing drug effects on disease progression in longer trials.

Quantifying the probability of pharmacological success to inform compound progression decisions

The Probability of Pharmacology Success, or PoPS, is a powerful metric to inform progression decisions by quantifying a compound’s overall pharmacological strength based on its mechanism. It is defined as the probability that X level of pharmacology is achieved in Y proportion of patients at a safe dose. The importance of adequate drug exposure, target engagement and functional pharmacology for enabling a compound’s efficacy is widely recognized. The PoPS estimates how well these conditions are met by integrating the compound’s pharmacological properties and the target’s modulation needs for the intended indication, in a pharmacometric model that includes the knowledge uncertainty. We use examples to illustrate how it can be used to compare drug candidates under specified benefit and risk conditions, support first-in-human decisions based on exposure limits, advise preclinical lead optimisation, and define clinical-trial populations.

Eigenfactor score and alternative bibliometrics surpass the impact factor in a 2-years ahead annual-citation calculation: a linear mixed design model analysis of Radiology, Nuclear Medicine and Medical Imaging journals

INTRODUCTION

Because we believe the journal selection before a manuscript submission deserves further investigation in each medical specialty, we aimed to evaluate the predictive ability of seven bibliometrics in the Radiology, Nuclear Medicine and Medical Imaging category of the Web of Knowledge to calculate total citations over a 7-year period.

METHODS

A linear mixed effects design using random slopes and intercepts were performed on bibliometrics corresponding to 124 journals from 2007 to 2011, with their corresponding citations from 2009 to 2013, which appeared in the Journal Citations Report Science Edition.

RESULTS

The Eigenfactor Score, Article Influence Score, Cited Half-life, 5-years impact factor and Number of Articles are significant predictors of 2-year-ahead total citations (p ≤ 0.010 for all variables). The impact factor and Immediacy Index are not significant predictors. There was a significant global effect size (R2 = 0.934; p < 0.001), which yielded a total variance of 93.4%.

CONCLUSIONS

Our findings support researchers' decision to stop the misuse of IF alone to evaluate journals. Radiologists and other researchers should review journal's bibliometrics for their decision-making during the manuscript submission phase. A re-ranking of journals using Eigenfactor Score, Article Influence Score, and Cited Half-life provides a better assessment of their significance and importance in particular disciplines.