Application of applied statistics in the pharma industry

Applied statistics in the pharmaceutical industry is traced to Kefauver-Harris Drug Amendments in 1962. According to the amendment, firms must prove the safety of the drug and also present substantial evidence of its efficiency (Burger et al, 2012). This led to an increased need for statisticians, especially with the adoption of genomics and proteomics for drug discovery.

The drug development lifecycle is between 12 and 14 years before a marketing process is initiated and pharmaceutical statisticians are involved in all stages of drug development process i.e. from drug discovery to post-marketing strategies (Cleophas et al 2009). Thus, statistics can be applied in case of both clinical and non-clinical processes within the pharmaceutical industry.

Flowchart reflecting application of applied statistics in pharmaceutical industry

Flowchart reflecting an application of applied statistics in the pharmaceutical industry

Applied statistics for clinical applications

Clinical testing is a step by step process wherein the effect of the pharmaceutical product on humans is studied. In the initial stage, several trials evaluate pharmacokinetics and pharmacodynamics of the drug. This is followed by efficiency studies to determine the dose-range and drug-drug interactions, which is done using applied statistics. Establishing the efficacy of the drug is important before confirmatory tests to determine the safety of the drugs among a heterogeneous population set. At the confirmatory stage, again, most of the studies are based on statistical analysis. One considers several factors before evaluating the trial design (Cleophas, 2009; Burger et al 2012; Burger 2013). These include:

  • Overall clinical development i.e. trial context, the scope of trials, and design to avoid bias.
  • Trial design i.e. design configurations, multi-centre trials, type of comparison, sample size, data capture and processing.
  • Trial conduct i.e trial monitoring and interim analysis, accrual rates, defining inclusion and exclusion criteria, sample size adjustment and interim analysis.
  • And data analysis and safety and tolerability of the drug (ICH-E9) [1].

A new molecular or biological entity must be confirmed before the trial efficacy begins. At this stage, it is important to conduct risk-benefit and cost-effectiveness analyses, requiring expertise from the statistician. Thus, multiple trial studies can test the contraindications of the drug. Further, the statisticians can help in a promotional review of the drug and contribute to epidemiological and pharmacovigilance studies.

Applied statistics for non-clinical applications

The pharmaceutical industry is undergoing a significant change with new treatments becoming increasingly complex and costly. These changes within the dynamic business environment, provide opportunities to statisticians within the pharmaceutical industry. Furthermore, statistical tasks within non-clinical applications consist of strategic and operational tasks. While strategic tasks reflect the impact of the drug design at the compound level, operational tasks, on the other hand, deal with the implementation of the design (Burger 2013). In the initial stage of drug designing, applied statistics can help in:

  • screening,
  • chemical development,
  • drug delivery process designing,
  • assay development and
  • formulation development.

The frequent need for applied statistics at this stage is to facilitate entry into unknown markets and for non-clinical trials. At stage 1 (Refer to the above figure) quantification of gene expression, prediction of rare disease, identification of biomarkers is some of the key areas where biostatisticians are useful. Furthermore, post-drug-discovery, it is imperative for pharmaceutical companies to identify the key market for their drug before they begin their marketing process. Therefore, applied statistics are important to determine market management strategies to maximize ROI, market expansion strategies, strategies to enhance brand impact, create value, optimize resources, maximize the value of the launch, improve workforce efficiency, reduce attrition rates and improve customer service and purchase behaviour. For example, if Company X is retailing its product in both rural and urban markets and needs to determine the factors influencing the purchase behaviour more so among the rural population, they would need statisticians to identify the key factors influencing the rural population, competitors of the company and the product in the urban market and also enable them in devising strategies to overcome related challenges.

Reference

  1. ICH Topic E 9 Statistical Principles for Clinical Trials, (1998), European Medicines Agency Available at: http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2009/09/WC500002928.pdf.
  2. Burger H.U., (2013) Statisticians in the Pharmaceutical Industry, Conference Proceedings at SSG, October 18 2013.
  3. Burger H.U., Driessen S., Fletcher C., Gerlinger C. and Branson M. (2012). Roles and career paths for statisticians in today’s pharmaceutical industry, EFSPI report 2012.
  4. Cleophas T.J. Zwinderman A.H., Cleophas T.F. Cleophas EP. (2009). Statistics Applied to Clinical Trials. 4th Ed. Springer, Netherlands.

Avishek Majumder

Research Analyst at Project Guru
Avishek is a Master in Biotechnology and has previously worked with Lifecell International Private Limited. Apart from data analysis and biological research, he loves photography and reading. He loves to play football and basketball in his spare time with an avid interest in adventure and nature. He was also a member of the Scouts in his school and has attended Military training.
Avishek Majumder

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