While the potential for the application of pharmacogenomics and theranostics to develop personalized healthcare solutions is enormous, multiple challenges will need to be addressed to get there. Understanding the complex interactions and detailed characterization of the functional variants of individual ADME (Absorption Distribution Metabolism Excretion) genes and drug target genes is needed to demonstrate clinical utility, using both a bottoms-up as well as a top–down approach. Clinical trials need to be designed appropriately so as to identify not only individual but also population variations. The impact of non-genetic and environmental factors, epigenetic variations and circadian rhythms on an individual's response need to be assessed to make pharmacogenomics clinically indicated. More advanced algorithms and appropriate study designs need to be developed to allow this pipeline to grow and to be used effectively in the clinical setting.Another challenge lies in the value proposition to the pharmaceutical industry. Fearing the impact of the slice and dice approach on revenues, companies are going slow on developing pharmacogenomic solutions; yet many are hedging their bets, amassing huge amounts of single nucleotide polymorphisms (SNP) data. They are being used as predictors of drug efficacy and safety to zero in on subpopulations that are at risk for either a bad response or no response in clinical trials, supporting the Fail fast, Fail cheap approach. In addition, the growth of theranostics is impeded by the fear that the approval of both the diagnostic and the drug would get delayed. Education of the health care provider, payor, regulator and the patient is also required and an exercise of change management needs to occur.Countries such as India should exploit the joint benefit of the reduced cost of tests today, complemented by a large and a highly genetically diverse population.
With the focus of the COVID-19 pandemic, we wanted to reach all stakeholders representing communities concerned with good clinical data management practices. We wanted to represent not only data managers but bio-statisticians, clinical monitors, data scientists, informaticians, and all those who collect, organize, analyze, and report on clinical research data. In our paper we will discuss the history of clinical data management in the US and its evolution from the early days of FDA guidance. We will explore the role of biomedical research focusing on the similarities and differences in industry and academia clinical research data management and what we can learn from each other. We will talk about our goals for recruitment and training for the CDM community and what we propose for increasing the knowledge and understanding of good clinical data practice to all – particularly our front-line data collectors i.e., nurses, medical assistants, patients, other data collectors. Finally, we will explore the challenges and opportunities to see CDM as the hub for good clinical data research practices in all of our communities.We will also discuss our survey on how the COVID-19 pandemic has affected the work of CDM in clinical research.
Risk-based monitoring (RBM) has disrupted the clinical trial industry, challenging conventional monitoring norms, business processes, and organizational structures. Endorsed by regulators and leading industry forums, and further driven by escalating drug development costs and enabling technology shifts making data available real time, the industry is moving from a mode of recalcitrance to acceptance. The effective implementation of RBM requires delicately interweaving changes in technology, processes, people, and perspectives. This article deliberates upon the multiple challenges that exist and proposes potential solutions.
The panel members respond to questions that have been raised during sessions on different developments in using technology in medical writing. Medical writing is a profession dedicated to transforming data and analyses into useful and digestible information, whether that information involves regulatory applications or documents for the public and, specifically, patients. Decisions about treatments or granting of approvals depend on the distillation being accurate, clear, and understandable. Using available technology well can support this goal in that it is a means for shifting a writer’s focus from what can be accomplished by artificial intelligence (AI), machine learning, and functions to the creation of content.
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