Pharmaceutical Medicine最新文献

For decades, oncology research has primarily relied on survival-based endpoints, such as progression-free survival and overall survival, to evaluate treatment efficacy. However, recent studies and international guidelines underscore the importance of incorporating patient-reported outcomes through patient-reported outcomes measures (PROMs). PROMs provide a more comprehensive view of treatment effectiveness, integrating the concepts of 'living longer' and 'living better.' Health-related quality of life (HRQoL) improvements have an intrinsic value for the patient, with importance in the overall definition of treatment value. These findings have sparked discussions regarding the relationship between HRQoL and traditional survival endpoints, influencing both oncology clinical trials and their interpretation for decision-making processes in practice. To effectively integrate PROMs into research, the choice of study design, appropriate PROMs questionnaires, and timing of administration are critical. The clinician's ability to interpret HRQoL data with awareness is equally important to ensure good clinical decision making. A pivotal concept in this context is the minimum clinically important difference (MCID), which is essential to inform the interpretation of treatment effect size in terms of clinically relevant HRQoL changes. Incorporating PROMs fosters a patient-centered approach to cancer care, aligning treatment goals with individual preferences and values. By balancing survival outcomes with quality of life, and through empathetic communication, healthcare providers can deliver treatments that are not only effective but also resonate with patients' experiences and priorities.

Collaborations between academia and the pharmaceutical industry are common for drug development and clinical trials, but rare for pharmacovigilance. Here we describe a novel model for collaboration between academia and the pharmaceutical industry, focused on post-marketing pharmacovigilance, that others may wish to emulate. For the past 5 years, Otsuka Pharmaceutical, a global Japan-based pharmaceutical company, has collaborated with faculty at Columbia University, a major university, for epidemiology support. The primary aim of this collaboration is to provide expert research guidance for Otsuka's pharmacovigilance group on questions involving pharmacoepidemiology. University epidemiologists are also consulted by other industry divisions, such as the clinical trials group. University epidemiologists help to determine the incidence, prevalence, and outcomes of diseases; draft the epidemiology components of risk management plans for drugs; and plan retrospective database analyses. A second major aim of this collaboration is to provide educational services to the company by conducting workshops on basic epidemiology and biostatistics; leading a monthly lecture/journal club series; hosting seminars on medical topics; and providing a writing workshop to assist in preparing abstracts and papers for presentation and publication. University epidemiologists provide oversight/evaluation through quarterly presentations and updates to the industry partner's external advisory committee as well as to university leadership. This type of epidemiologic collaboration has key advantages for industry over the alternatives of building an in-house epidemiology department or hiring outside consulting firms: lower cost; rapid access to university experts for potentially esoteric medical topics; and, for education, deep university experience in terms of assembling didactic programming and recruiting speakers. We offer this model for collaboration for others performing mandatory regulatory post-marketing pharmacovigilance activities.

Health economics and outcomes research (HEOR) has become an integral part of healthcare systems, through its ability to authentically demonstrate the value of the product. HEOR provides healthcare stakeholders with important insights to make informed decisions regarding healthcare delivery. This review aims to highlight the pivotal role of HEOR across the product lifecycle and the value of integrating HEOR activities during the various phases of drug development. Pharmaceutical companies are increasingly realizing that the integration of HEOR activities from early phases of product development through product launch, also during the postmarketing phase, to generate real-world evidence (RWE) can be crucial for their product's continued commercial success. HEOR helps validate the value of a pharmaceutical product, enabling its success in distinct regulatory and health technology assessment (HTA) landscapes across varied geographies. Regardless of several challenges in data collection and analysis, technological advancements facilitate opportunities to improve the value of HEOR. With rising demands for robust clinical evidence by global regulators and economic evidence by HTA agencies and payers, HEOR will become even more crucial in establishing long-lasting value of a pharmaceutical product for all stakeholders, including regulators, patients, prescribers, and payers.

Pharmacovigilance is the science of collection, detection, and assessment of adverse events associated with pharmaceutical products for the ongoing monitoring and understanding of those products' safety profiles. Part of this process, signal management, encompasses the activities of signal detection, signal validation/confirmation, signal evaluation, and ultimately, final assessment as to whether a safety signal constitutes a new causal adverse drug reaction. Artificial intelligence is a group of technologies including machine learning and natural language processing that are revolutionizing multiple industries through intelligent automation. Here, we present a critical evaluation of studies leveraging artificial intelligence in signal management to characterize the benefits and limitations of the technology, the level of transparency, and our perspective on best practices for the future. To this end, PubMed and Embase were searched cumulatively for terms pertaining to signal management and artificial intelligence, machine learning, or natural language processing. Information pertaining to the artificial intelligence model used, hyperparameter settings, training/testing data, performance, feature analysis, and more was extracted from included articles. Common signal detection methods included k-means, random forest, and gradient boosting machine. Machine learning algorithms generally outperformed traditional frequentist or Bayesian measures of disproportionality per various metrics, showing the potential utility of advanced machine learning technologies in signal detection. In signal validation and evaluation, natural language processing was typically applied. Overall, methodological transparency was mixed and only some studies leveraged "gold standard" publicly available positive and negative control datasets. Overall, innovation in pharmacovigilance signal management is being driven by machine learning and natural language processing models, particularly in signal detection, in part because of high-performing bagging methods such as random forest and gradient boosting machine. These technologies may be well poised to accelerate progress in this field when used transparently and ethically. Future research is needed to assess the applicability of these techniques across various therapeutic areas and drug classes in the broader pharmaceutical industry.

Disproportionality analysis is a method of safety signal detection based on quantitative analysis of spontaneous reports of adverse events. Disproportionality findings are often presented in medical publications as real-world evidence on drug safety. In this paper, we review theoretical properties of disproportionality analysis in the framework of causal inference theory. We show that measures of disproportionality can approximate the causal rate ratio for a specific drug-event combination when the study drug and the set of comparator drugs satisfy all of the following conditions: (1) there is no uncontrolled confounding for the drug-event association of interest, (2) under-reporting for the event of interest is either absent or has the same relative magnitude for the study drug and for the comparator drugs, and (3) reporting rates for all adverse events combined are the same for the study drug and for the comparator drug set. Because these conditions are typically not even approximately satisfied in practice, the overwhelming majority of disproportionality hits represent statistical noise rather than causal associations. Researchers choosing to report disproportionality findings in publications should explicitly acknowledge all key assumptions and the exploratory nature of this data-mining technique.

This paper provides an overview of the ethical considerations surrounding the inclusion of underserved populations in later-phase clinical trials. Underserved populations, defined here as those with restricted access to or limited benefits from healthcare, often face systemic, logistical, and social barriers that limit their participation in research. This results in a lack of representation that undermines fairness in research and also hampers the development of effective inclusive healthcare practices. This paper argues that including underserved populations in research is crucial for promoting justice, increasing the generalizability of research findings, and building trust in medical institutions. It differentiates underserved populations from other populations of interest, including vulnerable, minority, and underrepresented groups. It then explores barriers to research participation and targeted solutions for four underserved populations: rural residents, racial and ethnic minorities, low-income individuals, and older adults. Strategies for improving participation include expanding trial sites to accessible locations, lowering financial and logistical barriers, broadening eligibility criteria, and fostering culturally tailored outreach and engagement. While some interventions may apply broadly across groups, effective solutions will often require intersectional and context-specific strategies tailored to each population's unique needs as well as coordinated efforts from multiple stakeholders. While these interventions alone cannot resolve healthcare inequities - as underrepresentation of underserved populations in research is just one contributing factor - their widespread implementation would represent meaningful steps toward advancing health equity.

Much infectious disease research focuses on the interaction of microorganisms and drugs in the laboratory, assuming biological activity of inhibiting organism growth in vitro directly translates to improving patient outcomes in the clinic. Yet in vitro testing does not consider the important role of the human immune system in causing and response to disease. Research shows that patient outcomes are still suboptimal even with disease due to organisms that maintain in vitro susceptibility to currently available drugs. Resources and discussions have focused on "antimicrobial resistance" yet the majority of deaths are with susceptible organisms. Studies of new interventions do not address the questions that patients and clinicians in practice ask in order to improve patient outcomes regardless of causative pathogen in patients who would receive the drugs in the real-world setting. Research in infectious diseases should shift to refocus on improving patient outcomes. This would result in changes in the research questions evaluated, the types of patients enrolled, the comparisons made, the interventions studied, the outcomes evaluated, and the types of statistical evaluations used. In turn this would provide patients and clinicians with better evidence for patient care and justify payment for new interventions.

Background and objectives: The inherited backlog of 16,000 medicines applications of the South African Health Products Regulatory Authority (SAHPRA) was cleared through facilitated review pathways that included reliance on prior work by trusted regulators. This research aimed at determining the economic impact of reliance on national regulatory authorities (NRAs) in terms of lower assessors' costs, especially to offset the financial efforts required to attain a higher World Health Organization (WHO) maturity level and understanding the way fees can sustain NRA activities.

Methods: To this end, the assessor costs associated with reliance and full review applications were calculated and compared. A high-level review of African NRA fee structures was also carried out and pharmaceutical industry input was solicited regarding the feasibility of alternative tariff modalities for low- and middle-income (LMIC) NRAs.

Results: The investigation showed a marked reduction in time spent in reliance assessments compared to full reviews, with an associated decrease in reviewers' costs; SAHPRA conserved US$277,413 across the 188 applications applying reliance principles. The NRA fee structure review revealed outdated fees with little differentiation between full and reliance assessment. NRAs lack the financial resources to strengthen regulatory systems; WHO Global Benchmarking Tool activities are not directly covered by levied fees. Overall, the pharmaceutical industry was supportive of advancing the maturity of African NRAs and was willing to pay increased fees for reliance reviews when authorities adhere to published timelines. More expensive fast-track services were cited, making an argument for higher fees for reliance assessment when this enables medicines to reach markets quicker.

Conclusions: Reliance is a tool to safeguard NRA resources and support regulatory and information systems strengthening. The study illustrates the return on investment of reliance for NRAs and, if optimally implemented, the benefits for patients.