150 research outputs found

    Just Say No: The Case Against the Reclassification of Buprenorphine

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    The 340B Drug Pricing Program: Administration, Litigation, and Reform

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    Transformative Models to Promote Prescription Drug Innovation and Access: A Landscape Analysis

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    The patent-based pharmaceutical innovation system in the US does not incentivize the development of drugs with the greatest impact on patient or public health. It has also led to drug prices that patients and health care systems cannot afford. Three alternate approaches to promoting pharmaceutical innovation have been proposed to address these shortcomings. Delinkage models involve payments for drug innovation based on public health value rather than on a per-use basis. Public manufacturing models call upon governments and nonprofit organizations to lead drug discovery, development, and production. Public-private partnership models entail publicly-funded organizations working closely with for-profit partners on drug development and price-setting. Each model exhibits promise in promoting prescription drug innovation and access. This paper reviews these transformative models in detail, examining their key characteristics, advantages, and limitations

    Battling Over Patents: The Impact of Oil States on the Generic Drug Industry

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    In the 2018 case of Oil States Energy Services v. Greene’s Energy Group, the U.S. Supreme Court upheld the constitutionality of inter partes review, a non-judicial proceeding for challenging patents that was created by Congress as part of the 2011 Leahy-Smith America Invents Act. By establishing inter partes review, Congress hoped to rebalance patent policy to make it faster and less costly to invalidate erroneously granted patents in all fields of technology. In the pharmaceutical industry, generic drug companies have embraced inter partes review, filing hundreds of challenges in the first five years after its creation, with moderate success. Biologics, which make up a growing class of pharmaceutical products, are sometimes covered by dozens or scores of patents. As more of these complex therapeutics are developed and approved, inter partes review is expected to play an increasingly important role

    Use of Artificial Intelligence in Drug Development

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    Considerable focus has been placed on the health care applications of artificial intelligence (AI). Already, machine learning, a subset of AI that involves “the use of data and algorithms to imitate the way that humans learn” has been used to predict diseases, while AI-powered smartphone apps have been developed to promote mental health and weight loss. Owing in part to such successes, the market for AI in health care has been forecasted to increase more than 1000% between 2022 and 2029, from 13.8billionto13.8 billion to 164.1 billion. One area of substantial promise is drug development, which is poised to benefit from advances in the use of AI to predict protein folding, molecular interactions, and cellular disease processes. Successful application of AI to drug development, however, faces several obstacles, including poor model performance caused by nondiverse training data and shortcut learning. Additionally, the often opaque ways that AI systems reach their predictions conflict with regulatory approval frameworks that require a rationale for decision-making. Given these obstacles, we sought to identify the scope and breadth of AI use in drug development

    Crowdsourcing Public Health Experiments: A Response to Jonathan Darrow\u27s Crowdsourcing Clinical Trials

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    We are pleased to have this opportunity to respond to Jonathan Darrow\u27s article, Crowdsourcing Clinical Trials (CCT).\u27 We seek to highlight its important contributions and to commence debate over some of its arguments. In particular, we qualify the ethical arguments that characterize early clinical use of drugs as if they were research, and suggest instead that, in either domain, the ethical (and legal) analysis should remain focused on whether all material information is provided so patients may make informed decisions. We also highlight the limits of what can be gleaned from the observational data collection efforts envisioned by CCT. Ultimately, we exploit the core insights of CCT to expand the potential use of crowdsourcing from observational studies to truly randomized interventional trials. Randomized experiments allow causal inference because they assign subjects to a treatment and control group, and collect data from each. Furthermore, we draw attention to the fact that much of public health is driven not by pharmaceuticals, but by lifestyle factors. We suggest that CCT\u27s envisioned platform for crowdsourcing also has great potential to engage the public in producing new and trustworthy knowledge in the domains of diet, exercise, nutritional supplements, and integrative medicine, which are primary drivers of health outcomes and spending

    COVID-19 vaccine boosters for all adults: An optimal U.s. approach?

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    By 20 October 2021, the U.S. Food and Drug Administration (FDA) had amended its Emergency Use Authorizations for immunocompetent adults who previously received the Pfizer-BioNTech, Moderna, or Johnson & Johnson COVID-19 vaccines. For the 2-dose Pfizer-BioNTech and Moderna vaccines, the FDA permitted a single booster dose for adults aged 65 years or older and adults aged 18 to 64 years at high-risk for severe COVID-19 or at high risk for occupational or institutional COVID-19 exposure. For the single-dose Johnson & Johnson vaccine, the FDA permitted a single booster dose for all adults aged 18 or older. These eligibility schemes were endorsed by the Centers for Disease Control and Prevention shortly after FDA approval

    Influence of drug safety advisories on drug utilisation: an international interrupted time series and meta-analysis

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    OBJECTIVE: To evaluate the association between regulatory drug safety advisories and changes in drug utilisation. DESIGN: We conducted controlled, interrupted times series analyses with administrative prescription claims data to estimate changes in drug utilisation following advisories. We used random-effects meta-analysis with inverse-variance weighting to estimate the average postadvisory change in drug utilisation across advisories. STUDY POPULATION: We included advisories issued in Canada, Denmark, the UK and the USA during 2009-2015, mainly concerning drugs in common use in primary care. We excluded advisories related to over-the-counter drugs, drug-drug interactions, vaccines, drugs used primarily in hospital and advisories with co-interventions within ±6 months. MAIN OUTCOME MEASURES: Change in drug utilisation, defined as actual versus predicted percentage change in the number of prescriptions (for advisories without dose-related advice), or in the number of defined daily doses (for dose-related advisories), per 100 000 population. RESULTS: Among advisories without dose-related advice (n=20), the average change in drug utilisation was -5.83% (95% CI -10.93 to -0.73; p=0.03). Advisories with dose-related advice (n=4) were not associated with a statistically significant change in drug utilisation (-1.93%; 95% CI -17.10 to 13.23; p=0.80). In a post hoc subgroup analysis of advisories without dose-related advice, we observed no statistically significant difference between the change in drug utilisation following advisories with explicit prescribing advice, such as a recommendation to consider the risk of a drug when prescribing, and the change in drug utilisation following advisories without such advice. CONCLUSIONS: Among safety advisories issued on a wide range of drugs during 2009-2015 in 4 countries (Canada, Denmark, the UK and the USA), the association of advisories with changes in drug utilisation was variable, and the average association was modest
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