Communities of Practice in the Public-Private-Partnership Sector for Neglected Diseases Drug Development: the Importance of Mindset Mapping

This research article explores the mindsets of Public-Private Partnerships and Clinical Trials Organizations (CTOs) and the potential conflicts when working on drug discovery and development in the Third World global infectious diseases sector. A Communities-of-Practice (CoP) approach has been adopted to more fully explore the underlying values, attitudes and practices of these two future partners. This exploratory study suggests that future collaboration will be dependent on the two communities understanding and interpretation of each others‟ sustainability drug development drivers. The authors present secondary research findings that suggest the positive contribution that cognitive mapping of a community‟s sense-making can have in understanding the community‟s likely engagement in any future joint enterprise. Proposed future research will explore the underlying sustainability drivers that may both push and pull CTOs to engage in future global infectious diseases discovery and development projects. The article concludes by discussing the implications for future sustainable drug development projects involving PPPs and potential new strategic partners

The Price of Progress: Funding and Financing Alzheimer\u27s Disease Drug Development

Introduction Advancing research and treatment for Alzheimer\u27s disease (AD) and the search for effective treatments depend on a complex financial ecosystem involving federal, state, industry, advocacy, venture capital, and philanthropy funding approaches. Methods We conducted an expert review of the literature pertaining to funding and financing of translational research and drug development for AD. Results The federal government is the largest public funder of research in AD. The National Institute on Aging, National Institute of Mental Health, National Institute of General Medical Sciences, and National Center for Advancing Translational Science all fund aspects of research in AD drug development. Non-National Institutes of Health federal funding comes from the National Science Foundation, Veterans Administration, Food and Drug Administration, and the Center for Medicare and Medicaid Services. Academic Medical Centers host much of the federally funded basic science research and are increasingly involved in drug development. Funding of the “Valley of Death” involves philanthropy and federal funding through small business programs and private equity from seed capital, angel investors, and venture capital companies. Advocacy groups fund both basic science and clinical trials. The Alzheimer Association is the advocacy organization with the largest research support portfolio relevant to AD drug development. Pharmaceutical companies are the largest supporters of biomedical research worldwide; companies are most interested in late stage de-risked drugs. Drugs progressing into phase II and III are candidates for pharmaceutical industry support through licensing, mergers and acquisitions, and co-development collaborations. Discussion Together, the funding and financing entities involved in supporting AD drug development comprise a complex, interactive, dynamic financial ecosystem. Funding source interaction is largely unstructured and available funding is insufficient to meet all demands for new therapies. Novel approaches to funding such as mega-funds have been proposed and more integration of component parts would assist in accelerating drug development

Privacy and Accountability in Black-Box Medicine

Black-box medicine—the use of big data and sophisticated machine learning techniques for health-care applications—could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and unbiased, but this means giving outsiders access to this health information. This article examines the tension between the twin goals of privacy and accountability and develops a framework for balancing that tension. It proposes three pillars for an effective system of privacy-preserving accountability: substantive limitations on the collection, use, and disclosure of patient information; independent gatekeepers regulating information sharing between those developing and verifying black-box algorithms; and information-security requirements to prevent unintentional disclosures of patient information. The article examines and draws on a similar debate in the field of clinical trials, where disclosing information from past trials can lead to new treatments but also threatens patient privacy

Representation learning of drug and disease terms for drug repositioning

Drug repositioning (DR) refers to identification of novel indications for the approved drugs. The requirement of huge investment of time as well as money and risk of failure in clinical trials have led to surge in interest in drug repositioning. DR exploits two major aspects associated with drugs and diseases: existence of similarity among drugs and among diseases due to their shared involved genes or pathways or common biological effects. Existing methods of identifying drug-disease association majorly rely on the information available in the structured databases only. On the other hand, abundant information available in form of free texts in biomedical research articles are not being fully exploited. Word-embedding or obtaining vector representation of words from a large corpora of free texts using neural network methods have been shown to give significant performance for several natural language processing tasks. In this work we propose a novel way of representation learning to obtain features of drugs and diseases by combining complementary information available in unstructured texts and structured datasets. Next we use matrix completion approach on these feature vectors to learn projection matrix between drug and disease vector spaces. The proposed method has shown competitive performance with state-of-the-art methods. Further, the case studies on Alzheimer's and Hypertension diseases have shown that the predicted associations are matching with the existing knowledge.Comment: Accepted to appear in 3rd IEEE International Conference on Cybernetics (Spl Session: Deep Learning for Prediction and Estimation

2008 Progress Report on Brain Research

Highlights new research on various disorders, nervous system injuries, neuroethics, neuroimmunology, pain, sense and body function, stem cells and neurogenesis, and thought and memory. Includes essays on arts and cognition and on deep brain stimulation

Antiretroviral Non-Adherence is Associated With a Retrieval Profile of Deficits in Verbal Episodic Memory.

HIV-associated deficits in verbal episodic memory are commonly associated with antiretroviral non-adherence; however, the specific aspects of memory functioning (e.g., encoding, consolidation, or retrieval) that underlie this established relationship are not well understood. This study evaluated verbal memory profiles of 202 HIV+ participants who underwent a 30-day electronic monitoring of antiretroviral adherence. At the group level, non-adherence was significantly associated with lower scores on immediate and delayed passage recall and word list learning. Retention and recognition of passages and word lists were not related to adherence. Participants were then classified as having either a normal verbal memory profile, a "subcortical" retrieval profile (i.e., impaired free recall with relatively spared recognition), or a "cortical" encoding profile (e.g., cued recall intrusions) based on the Massman et al. ( 1990 ) algorithm for the California Verbal Learning Test. HIV+ participants with a classic retrieval deficit had significantly greater odds of being non-adherent than participants with a normal or encoding profile. These findings suggest that adherence to prescribed antiretroviral regimens may be particularly vulnerable to disruption in HIV+ individuals due to deficits in the complex process of efficiently accessing verbal episodic information with minimal cues. A stronger relationship between non-adherence and passage (vs. word list) recall was also found and may reflect the importance of contextual features in remembering to take medications. Targeted interventions for enhancing and supporting episodic memory retrieval processes may improve antiretroviral adherence and overall health outcomes among persons living with HIV