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Introduction
Published in Andrew P. Grieve, Hybrid Frequentist/Bayesian Power and Bayesian Power in Planning Clinical Trials, 2022
In Chapter 9, we generalise some of the results from earlier chapters to studies in which multiple decision criteria, based on statistical significance and relevance, are used. Such considerations are particularly relevant in the context of proof-of-concept trials.
Breaking down silos
Published in Paul M.W. Hackett, Christopher M. Hayre, Handbook of Ethnography in Healthcare Research, 2020
I have witnessed this corporate mindset and behaviour towards applied ethnography and qualitative design research methods many times. When it comes to innovation and business (strategy) development, there is a focus on proof of concept. I will suggest that a focus should be dedicated to proof of problem too. We need to know the problem in order to develop the right solution. In my opinion, innovation in a business context, and health innovation belongs here as well, these need to be re-invented to cope with the increasing complexity of our world today. To proof a problem, in my view, means exactly opening up to its connections and disconnections, its movements and the living culture it is a part of, and thereby exploring its innovation potential. We need to engage ourselves with people, patients, or consumers, as well as the larger systems that surround them. We need to think about innovation in a more systemic way too.
Clinical Development in the Light of Bayesian Statistics
Published in Emmanuel Lesaffre, Gianluca Baio, Bruno Boulanger, Bayesian Methods in Pharmaceutical Research, 2020
Early phase proof of concept studies can provide an opportunity to incorporate historical information from previous studies of the same disease. Often the control treatment or placebo group will be the same in multiple historical studies. This allows the development of an informative prior for the control arm based on synthesis of the control group data from the series of relevant previous trials. As a result, the control group in the new trial can be reduced by the number of virtual patients represented by the historical prior.
Preclinical target validation for non-addictive therapeutics development for pain
Published in Expert Opinion on Therapeutic Targets, 2022
Richard Hargreaves, Karen Akinsanya, Seena K. Ajit, Neel T. Dhruv, Jamie Driscoll, Peter Farina, Narender Gavva, Marie Gill, Andrea Houghton, Smriti Iyengar, Carrie Jones, Annemieke Kavelaars, Ajamete Kaykas, Walter J. Koroshetz, Pascal Laeng, Jennifer M. Laird, Donald C. Lo, Johan Luthman, Gordon Munro, Michael L. Oshinsky, G. Sitta Sittampalam, Sarah A. Woller, Amir P. Tamiz
Effective target validation and therapeutic development is a stepwise process. Early in the development process, a drug candidate must demonstrate proof of target engagement. That is, the intervention must reach and be confirmed to interact with the target of interest. Once the therapy is confirmed to reach the target, proof of mechanism must be established, generally by showing an exposure-effect relationship through pharmacodynamics (PD) readouts. Biomarkers are often leveraged for target engagement studies, more specifically proof-of-presence and proof-of-mechanism studies. Confirmation that the targeted mechanism of action influences the intended pathophysiology at well-tolerated doses is addressed next in proof of principle studies. Proof of concept studies validate that the mechanism of action can be used to safely treat the disease indication through meaningful improvements in clinically relevant endpoints.
A critical review of apomorphine hydrochloride sublingual film for the treatment of Parkinson’s disease ‘OFF’ episodes
Published in Expert Review of Neurotherapeutics, 2021
Christopher Y. Caughman, Stewart Factor
In 2016, a phase 2, open-label, proof of concept study was able to show safety, tolerability, and efficacy [34]. Subjects were required to have at least 1 ‘off’ episode/day and more than 2 hours of daily ‘off’ time. Twenty subjects entered clinic in the practically defined ‘off’ state (‘off’ for 12 hours). They were pre-treated for three days with trimethobenzamide then dosed (strip placed under the tongue and allowed to dissolve over 2 mins) with doses started at 10 mg. If a full ‘on’ state was not achieved within 3 hours, the dose was increased in 5 mg increments until a full ‘on’ was achieved or to a maximum dose of 30 mg. Assessments in the ‘off’ and ‘on’ states were with the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part III (motor examination) pre-dose and at 15, 30, 45, 60, and 90 minutes. If they had a full response, the dose was used a second time to confirm response. Subjects could be dosed up to two times a day over 3 days. Nineteen patients completed the study, 78.9% of these patients achieved a fully ‘on’ state, and all of these patients achieved this state within 30 minutes, 40% of which reached an ‘on’ state in less than 15 minutes [34]. Furthermore, ‘on’ times lasted for an average of 50 minutes, 60.0% remained fully ‘on’ for 90 minutes, and the average dose was 18.4 mg [34]. Within this cohort, 21.1% of patients reported nausea, but there were no reports of local oral mucosal irritation [34].
Pricing methods in outcome-based contracting: δ6: adherence-based pricing
Published in Journal of Medical Economics, 2020
Nimer S. Alkhatib, Marion Slack, Sandipan Bhattacharjee, Brian Erstad, Kenneth Ramos, Ali McBride, Ivo Abraham
Our proposed methodology has several strengths. The 7-step method yields plausible results. For the statistical methods, we used three predicted curves to estimate differences between the upper and mean curves, and between the lower curve in real-world data and the lower curve in clinical trial. The techniques and tools to digitizing, regression modeling, and curve fitting are commonly used in pharmacoeconomic modeling. In addition, the technique of MCS, in which sampling through iterations is considered, is applied to assess the stability of model inputs. The use of ranges for paybacks rather than absolute values provides flexibility, accommodates uncertainty, and considers that adherence in real settings may be different from those in clinical trials. Our proof-of-concept analysis considered two efficacy measures. The methods are treatment-specific regardless of market condition or competitors. This also compensates for a lack of comparative studies at the time of product launch, as the FDA regulators may not require an active comparator in clinical trials for approval purposes.