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Conduct Science promotes new generations of tools for science tech transferred from academic institutions including mazes, digital health apps, virtual reality and drones for science. Our news promotes the best new methodologies in science.
Conductscience Administrator
Conduct Science promotes new generations of tools for science tech transferred from academic institutions including mazes, digital health apps, virtual reality and drones for science. Our news promotes the best new methodologies in science.
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The Need for Real-world Data in Pharmaceutical Drug Discovery

Pharmaceutical drug products have the potential to eliminate diseases and increase life expectancy. New drugs must provide evidence of efficacy, effectiveness, and perceived value. At the same time, societies are struggling with the increasing costs and delays associated with drug discovery and marketing. Note that the average research and development cost for a new drug exceeds $2.6 billion. Factors, such as an increase in population aging, population growth, urbanization, obesity, mental illnesses, and prescription drugs bill, should also be considered by researchers, stakeholders, and authorities (Wise et al., 2018).

Consequently, more and more pharmaceutical companies and regulatory bodies have started to implement real-world data in drug discovery and development. Real-world data is defined as medical information collected in non-experimental conditions and across heterogeneous populations. Sources of real-world data vary from electronic health records to administrative claims. Appropriate tools to incorporate, analyze, and validate real-world data across various sectors (e.g., biostatistics) also become integrated into practice. Research findings reveal that real-world data has numerous benefits. Early access to medication schemes, for instance, is an essential factor which can advance drug discovery and public health. As a result, regulatory bodies worldwide are becoming more flexible and transparent. Note that to avoid conflicting guidance, a consensus between local and international agencies must be reached. Health technology assessment agencies, in particular, support the successful implementation of technology and real-world evidence. Since real-world data in drug discovery can accelerate the entire cycle of drug development, biopharmaceutical companies need to embrace real-world evidence and patient-centric models in digital health in order to provide a balance between costs, drug effectiveness, and perceived value.

Real-world Data in Pharmaceutical Drug Discovery: Areas of Implementation

Real-world data can benefit numerous aspects of research, including drug discovery. Drug discovery can be defined as the process of identifying new compounds and medications. Once a novel compound has been identified, the process of drug development can continue to establish a new medication on the market. Note that the newest advancements in scientific research and health technology have shifted the focus of research. Scientists focus on how a certain disease can be controlled on a molecular level and employ real-world data to support continuing research. Getting a better understanding of any disease is essential. Therefore, it’s alarming that, according to recent findings, seven out of eight compounds used in the clinical testing pipeline might be unsuccessful. Although pharmaceutical companies tend to employ real-world data mainly in post-marketing research, real-world evidence becomes eminent. Real-world data can benefit the entire cycle of drug discovery and development, with the following areas of implementation  (Wise et al., 2018):

• Real-world Data, Drug Discovery, and Clinical Trials:

Clinical research is complex. Randomized clinical trials are still defined as the gold standard in research. Although controlled medical studies provide valuable insights into drug efficacy, data obtained from real-world settings and diverse populations becomes mandatory to assess drug effectiveness. When it comes to drug discovery, real-world data can complement scientific findings and help researchers generate hypotheses and recruit participants. Since pharmaceutical bodies need to accommodate real-world data, pragmatic trials (that assess drug effectiveness in routine clinical practice) become a valuable approach. Pragmatic trials can decrease costs and improve generalizability because the focus of research is no longer on highly selective samples or strict inclusion/exclusion criteria. Note that a recent study, which tested a new inhaled therapy for chronic obstructive pulmonary disease, assessed drug effectiveness in real settings, revealing that real-world data can decrease costs and improve health outcomes. In fact, real-world data can benefit both the pre- and post-approval process for new drugs. In the post-approval aspect, for instance, real-world evidence can help experts reach a balance between drug safety, governmental regulations, and patient outcomes.

Real-world Data and Pharmacoepidemiology

Real-world data in drug discovery can benefit pharmacoepidemiology (Toh, 2017). Note that pharmacoepidemiology is an ambitious discipline that connects pharmacology and epidemiology. One of the main goals in pharmacoepidemiology is to assess the benefits of a new drug in large populations. It’s interesting to mention that pharmacovigilance is defined as a subdiscipline of  pharmacoepidemiologyp. Recent findings show that more and more regulatory bodies and research initiatives, such as the European Union-Adverse Drug Reaction project, integrate real-world data and mining of clinical databases in scientific knowledge. Such data can help experts explore drug safety and drug discovery. What’s more, with the increasing use of electronic health records and digital health tools in practice, smart real-world data becomes essential in medical research.

Real-world Data and Disease Taxonomy

Real-world data can improve the entire nature of disease taxonomy or disease classification. With novel medical practices focusing on the molecular level of diseases, the current Classification of Diseases (ICD) has started incorporating molecular findings to improve disease classification and drug discovery. This approach will help researchers plan a clinical trial based on relevant characteristics and allow participants to access effective treatments. Since real-world data covers a wide variety of sources (e.g., medical history, patient outcomes), stakeholders, researchers, and patients are willing to share and employ real-world data in order to benefit disease taxonomy and patient care.

Real-world Data and Quantitate Systems Pharmacology

Real-world data in drug discovery has a wide range of benefits, particularly in the field of quantitative systems pharmacology (Geerts & Spiros, 2015). Note that quantitative systems pharmacology aims to explore the effects of a novel drug by incorporating mathematical models, pharmacological information, and biological systems. In fact, this research discipline reveals numerous insights into drug design, biomarkers, and dosage. One of the challenges which quantitative systems pharmacology faces, though, is associated with data collection. Experts need to understand how to validate and incorporate real-world quantitative data. Since wearable devices and mHealth apps are among the most popular tools in health research and practice, data that comes directly from patients and their electronic devices can be abundant and unstructured. Security concerns and data privacy should be considered as well. Interestingly, recent studies show that when it comes to drug discovery, patients are willing to share their data and contribute to scie