Robust nonclinical data is an absolute requirement for building an effective translational strategy and developing clinically safe and effective medications. Most current efforts to facilitate generation of robust nonclinical research data focus on producing guidelines and checklists pertaining to study design and data analysis, but: a) do so only from a reporting perspective; b) do not consider processes other than study design and data analysis, such as compliance of data records with FAIR principles; and c) are at risk of triggering normative responses, whereby scientists simply satisfy the guidelines at a time when it is too late to take corrective actions. In this regard, and in line with RFA-DA-19-031, the current application proposes to develop, validate and introduce into common practice a novel web-based tool called PAASPort for early identification of potential risks of bias and support of decision making related to nonclinical research practice. PAASPort consists of a web application with proprietary analytics supporting project-specific and research unit-specific certification. In Phase 1, we will (Aim 1) convert the existing PAASPort prototype, which is currently a questionnaire in Microsoft Word format where data are manually organized and analyzed by a PAASP employee, into a web- based, interactive digital tool that can be accessed online at the research unit?s convenience and data will be automatically processed and prepared to provide feedback to the PAASPort customer (e.g., foundations, private investors, VCs, pharma);
(Aim 2) enhance the commercial value of PAASPort by asking different groups of private investors how they value the different aspects of bias in research practice related to 1) study design and conduct, 2) data integrity, 3) quality culture, and 4) organizational and individual factors. In Phase 2, we will:
(Aim 1) implement analytical mechanisms to support semi-automated processing of information collected from the online assessment;
(Aim 2) minimize normative responses to the online assessment by introducing onsite audit-based feedback control, and (Aim 3) demonstrate economic benefits of improved nonclinical research practice quality assessments for private investors. The tool will be developed and validated with neuroscience and, more specifically, substance use disorder drug discovery in focus, but it can be extrapolated to other disease indications and therapeutic areas. The most immediate impact of the proposed tool is to avoid normative responses to emerging standards in research quality. However, given that PAASPort-supported data are expected to be more robust, the ultimate goal is to facilitate decision making, industry-academia partnerships, and the advancement of fundamental discoveries into commercial development.
The lack of quality control measures in nonclinical biomedical research, a $56 billion market in the US alone, contributes to unreproducible data and increases the risk that subsequent clinical research and development of novel treatments for substance use disorders and other disorders will fail. We propose to develop and validate a novel research evaluation tool (PAASPort) for early identification of potential risks of bias related to nonclinical research practice. Created by industry and academic scientists working with business professionals, PAASPort will allow funding organizations such as private investors, non-profit foundations, corporate and non-corporate VCs to inform both financial (investment) and portfolio (science) decisions by supporting research with low risks of bias, thereby improving the probability that basic nonclinical discoveries will translate into safe and effective clinical treatments.
