A peer-reviewed, evidence-based journal for clinicians in the field of neuroscience
Issue link: http://innovationscns.epubxp.com/i/499434
Innovations in CLINICAL NEUROSCIENCE [ V O L U M E 1 2 , N U M B E R 3 – 4 , S U P P L E M E N T A , M A R C H – A P R I L 2 0 1 5 ] 8S to predict human drug response is shown in Figure 1. As with any analysis methodology, there are assumptions with varying levels of uncertainty. In this particular case, the greatest uncertainty was the translatability of the dog PK/PD relationship to humans. Based upon the available data, it was predicted that 25mg would be a sufficient dose to demonstrate a drug effect. However, earlier studies suggested 25mg would not be tolerated and so the dose was dropped to 10mg. This lower dose was still predicted to show an effect, but at a much lower magnitude. Contrary to expectations, the compound lowered biomarker concentrations in humans. The reason for this effect was unclear, but could have stemmed from too low a dose, the unprecedented mechanism, misspecification of the model, or small sample size. A second example comes from a proof of concept study in acute schizophrenia. The effects of Compound YYY with doses of 2, 5, and 15mg on PANSS total scores in patients with acute exacerbation of schizophrenia was compared with aripriprazole (15mg) as an active control. All treatments produced an improvement in PANSS score, but so did placebo. In fact, no separation of any treatments from placebo was observed using a linear trend test. Based upon a model-based meta- analysis of published acute schizophrenia data, the placebo effect was comparable with that of other publications, with simulations suggesting the results were plausible, although larger than reported by most studies. The effects of aripriprazole were also consistent with previous studies. Exposure- response analysis of Compound YYY data demonstrated that some components of the dose response, which would be critical for dose selection for the next study, were poorly characterized, thus identifying deficiencies in the learnings from the study. The examination of the effects of Compound YYY resulted in several lessons learned. Small studies can result in variable point estimates and have diminished power, while linear trend tests may not be sufficiently powerful for certain dose-response curves. Dose response effects are not always linear so relying on a hypothesis testing approach with rigid assumptions in a small POC study may not be the most efficient use of resources for learning about a compound in early development. In conclusion, employing model-based analyses can identify and bridge knowledge gaps where they exist, and inform quantitative, data-driven decisions while extracting further information. Sufficient sample sizes and knowledge of the dose-response e xpectancies are required CONDUCTING THE RIGHT PROOF- OF-CONCEPT STUDY It is important to recognize that drugs could be successfully tested in numerous indications, e.g., an alpha- 7 nicotinic acetylcholine receptor agonist could be useful for treating negative symptoms in schizophrenia and be pro-cognitive in patients with attention deficit hyperactivity disorder (ADHD) or Alzheimer's disease. One case study comes from proof-of-concept studies Merck conducted for an H 3 inverse agonist that targeted ADHD, was wake promoting in sleep apnea, and influenced cognition in Alzheimer's disease and schizophrenia. Numerous issues should be addressed to direct the development strategy, including target validation, how to assess target engagement and pharmacodynamics (PD) efficacy, and medical need. Examining data implicating the target in the disease pathophysiology is important. The clinical effects of H 1 antagonists include sedation and cognitive impairment, with weak evidence of weight gain, although some of these effects may be due to off-target anticholinergic effects. There is preclinical evidence, however, suggesting that H 1 agonists promote wakefulness. H 3 antagonists act on inhibitory and autoreceptors. 7 ABT- 239 is an H 3 antagonist that had positive effects on numerous preclinical models of behavior, with improvements in sensory gating, five- choice serial reaction-time task, y- maze alternation, and radial arm maze, 8,9 although not always beneficial. 10 The approach of Merck with MK-0249 was to replicate some of these findings and combine them with developing translatable biomarkers for target engagement and PD effects. In a Phase I program, multiple doses up to 12mg of MK-0249 were FIGURE 1. Road-map to predicting human drug response. A pharmacokinetic (PK) and pharmacodynamic (PD) model was developed to quantify PK and biomarker data in dogs. The dog PK/PD relationship along with first-in-human (FIH) PK and drug-free biomarker time course data were used to predict the mean biomarker response to the compound over time. The predicted dose-response and time course were used to design a human biomarker study.