Primer on Precision Medicine for Complex Chronic Disorders

David C Whitcomb, David C Whitcomb

Abstract

Precision medicine promises patients with complex disorders the right treatment for the right patient at the right dose at the right time with expectation of better health at a lower cost. The demand for precision medicine highlights the limitations of modern Western medicine. Modern Western medicine is a population-based, top-down approach that uses pathology to define disease. Precision medicine is a bottom-up approach that identifies predisease disorders using genetics, biomarkers, and modeling to prevent disease. This primer demonstrates the contrasting strengths and limitations of each paradigm and why precision medicine will eventually deliver on the promises.

Figures

Figure 1.
Figure 1.
Therapeutic trials using clinicopathologic disease criteria. (a) Randomized clinical trials attempt to reduce heterogeneity by selecting the maximum number of patients with the least variability in disease features using inclusion–exclusion criteria. In CCDs, the treatment response is mixed with the NNT >>1. The patients with the highest burden of disease and in need of effective treatment are excluded from traditional clinical drug trials. (b) The same disease population seen as a function of multiple underlying disorders (colored curves) that may be a function of a single or multiple factors. A RCT targeting a low-severity mechanism (blue curve) will have “strong evidence” of effectiveness in the RCT, but will be of no value in more severe disease mechanisms (yellow, orange, and red curves). New approaches are needed to apply drug trials to mechanisms rather than common symptoms. CCD, complex chronic diseases; NNT, number needed to treat; RCT, randomized controlled trial.
Figure 2.
Figure 2.
Effect of defining genetic risk factors in defined subpopulations to improve biomarker performance. In this example (a biomarker with a sensitivity of 85% and specificity of 85%), the identification of high-risk genetic risk factors moves a patient from a low-risk population (e.g., 1% prevalence) or patients with some disease symptoms (10% prevalence) to a subpopulation of patients with a high disease prevalence (e.g., 30%). Knowing the underlying mechanistic disorder through genetic analysis also adds specificity and also provides possible treatment targets.

References

    1. National Center for Chronic Disease Prevention and Health Promotion. Health and Economic Costs of Chronic Diseases () (2019). Accessed March 4, 2019.
    1. Atkinson AJJ, Colburn WA, DeGruttola VG, et al. Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin Pharmacol Ther 2001;69(3):89–95.
    1. Beck AH. STUDENT JAMA. The Flexner report and the standardization of American medical education. JAMA 2004;291(17):2139–40.
    1. Flexner A. Medical Education in the United States and Canada: A Report to the Carnegie Foundation for the Advancement of Teaching. Bulletin No. 4. New York, NY: The Carnegie Foundation for the Advancement of Teaching, p. 346, OCLC 9795002. 1910.
    1. Whitcomb DC. What is personalized medicine and what should it replace? Nat Rev Gastroenterol Hepatol 2012;9(7):418–24.
    1. Fedak KM, Bernal A, Capshaw ZA, et al. Applying the Bradford Hill criteria in the 21st century: How data integration has changed causal inference in molecular epidemiology. Emerg Themes Epidemiol 2015;12:14.
    1. Ha C, Ullman TA, Siegel CA, et al. Patients enrolled in randomized controlled trials do not represent the inflammatory bowel disease patient population. Clin Gastroenterol Hepatol 2012;10(9):1002–7.
    1. de Souza HS, Fiocchi C. Immunopathogenesis of IBD: Current state of the art. Nat Rev Gastroenterol Hepatol 2016;13(1):13–27.
    1. Hong M, Ye BD, Yang SK, et al. Immunochip meta-analysis of inflammatory bowel disease identifies three novel loci and four novel associations in previously reported loci. J Crohns Colitis 2018;12(6):730–41.
    1. Momozawa Y, Dmitrieva J, Theatre E, et al. IBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes. Nat Commun 2018;9(1):2427.
    1. Boeckmans J, Natale A, Buyl K, et al. Human-based systems: Mechanistic NASH modelling just around the corner? Pharmacol Res 2018;134:257–67.
    1. Buzzetti E, Pinzani M, Tsochatzis EA. The multiple-hit pathogenesis of non-alcoholic fatty liver disease (NAFLD). Metabolism 2016;65(8):1038–48.
    1. Sookoian S, Pirola CJ. Liver enzymes, metabolomics and genome-wide association studies: From systems biology to the personalized medicine. World J Gastroenterol 2015;21(3):711–25.
    1. Bonfiglio F, Henstrom M, Nag A, et al. A GWAS meta-analysis from 5 population-based cohorts implicates ion channel genes in the pathogenesis of irritable bowel syndrome. Neurogastroenterol Motil 2018;30(9):e13358.
    1. Whitcomb DC, Frulloni L, Garg P, et al. Chronic pancreatitis: An international draft consensus proposal for a new mechanistic definition. Pancreatology 2016;16:218–24.
    1. Whitcomb DC, Shimosegawa T, Chari ST, et al. International consensus statements on early chronic pancreatitis. Recommendations from the working group for the international consensus guidelines for chronic pancreatitis in collaboration with The International Association of Pancreatology, American Pancreatic Association, Japan Pancreas Society, Pancreasfest Working Group and European Pancreatic Club. Pancreatology 2018. [Epub ahead of print May 21, 2018.]
    1. Etemad B, Whitcomb DC. Chronic pancreatitis: Diagnosis, classification, and new genetic developments. Gastroenterology 2001;120:682–707.
    1. Whitcomb DC; North American Pancreatitis Study Group. Pancreatitis: TIGAR-O version 2 risk/etiology checklist with topic reviews, updates and use primers. Clin Translat Gastroenterol 2019;10:e00027.
    1. Schneider A, Lohr JM, Singer MV. The M-ANNHEIM classification of chronic pancreatitis: Introduction of a unifying classification system based on a review of previous classifications of the disease. J Gastroenterol 2007;42(2):101–19.
    1. Guda NM, Muddana V, Whitcomb DC, et al. Recurrent acute pancreatitis: International state-of-the-science conference with recommendations. Pancreas 2018;47(6):653–66.
    1. Vivian E, Cler L, Conwell D, et al. Acute pancreatitis task force on quality: Development of quality indicators for acute pancreatitis management. Am J Gastroenterol 2019. [Epub ahead of print June 12, 2019.].
    1. Gariepy CE, Heyman MB, Lowe ME, et al. Causal evaluation of acute recurrent and chronic pancreatitis in children: Consensus from the INSPPIRE group. J Pediatr Gastroenterol Nutr 2017;64(1):95–103.
    1. Parniczky A, Abu-El-Haija M, Husain S, et al. EPC/HPSG evidence-based guidelines for the management of pediatric pancreatitis. Pancreatology 2018;18(2):146–60.
    1. Lillie EO, Patay B, Diamant J, et al. The n-of-1 clinical trial: The ultimate strategy for individualizing medicine? Per Med 2011;8(2):161–73.
    1. Schork NJ. Personalized medicine: Time for one-person trials. Nature 2015;520(7549):609–11.
    1. Schork NJ, Goetz LH. Single-subject studies in translational nutrition research. Annu Rev Nutr 2017;37:395–422.
    1. Zardavas D, Piccart-Gebhart M. Clinical trials of precision medicine through molecular profiling: Focus on breast cancer. Am Soc Clin Oncol Educ Book 2015:e183–90.
    1. Cunanan KM, Iasonos A, Shen R, et al. An efficient basket trial design. Stat Med 2017;36(10):1568–79.
    1. Simon R. Critical review of umbrella, basket, and platform designs for oncology clinical trials. Clin Pharmacol Ther 2017;102(6):934–41.
    1. Kohane IS. Health Care Policy: Ten things we have to do to achieve precision medicine. Science 2015;349(6243):37–8.
    1. Whitcomb DC. Going MAD: Development of a “matrix academic division” to facilitate translating research to personalized medicine. Acad Med 2011;86(11):1353–9.

Source: PubMed

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