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Machine learning-guided, big data-enabled, biomarker-based systems pharmacology: modeling the stochasticity of natural history and disease progression

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Abstract

The incidence of systemic and metabolic co-morbidities increases with aging. The purpose was to investigate a novel paradigm for modeling the orchestrated changes in many disease-related biomarkers that occur during aging. A hybrid strategy that integrates machine learning and stochastic modeling was evaluated for modeling the long-term dynamics of biomarker systems. Bayesian networks (BN) were used to identify quantitative systems pharmacology (QSP)-like models for the inter-dependencies for three disease-related datasets of metabolic (MB), metabolic with leptin (MB-L), and cardiovascular (CVB) biomarkers from the NHANES database. Biomarker dynamics were modeled using discrete stochastic vector autoregression (VAR) equations. BN were used to derive the topological order and connectivity of a data driven QSP model structure for inter-dependence of biomarkers across the lifespan. The strength and directionality of the connections in the QSP models were evaluated using bootstrapping. VAR models based on QSP model structures from BN were assessed for modeling biomarker system dynamics. BN-restricted VAR models of order 1 were identified as parsimonious and effective for characterizing biomarker system dynamics in the MB, MB-L and CVB datasets. Simulation of annual and triennial data for each biomarker provided good fits and predictions of the training and test datasets, respectively. The novel strategy harnesses machine learning to construct QSP model structures for inter-dependence of biomarkers. Stochastic modeling with the QSP models was effective for predicting the age-varying dynamics of disease-relevant biomarkers over the lifespan.

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References

  1. World Health Organization (2018) Ageing and health. World Health Organization, Geneva

    Google Scholar 

  2. National Institute of Aging (2020) The National Institute On Aging: Strategic Directions For Research, 2020–2025: understanding the dynamics of the aging process. U.S, Department of Health and Human Services, Bethesda

    Google Scholar 

  3. Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G (2013) The hallmarks of aging. Cell 153(6):1194–1217

    Article  CAS  Google Scholar 

  4. Mangoni AA, Jackson SH (2004) Age-related changes in pharmacokinetics and pharmacodynamics: basic principles and practical applications. Br J Clin Pharmacol 57(1):6–14

    Article  CAS  Google Scholar 

  5. Shi S, Morike K, Klotz U (2008) The clinical implications of ageing for rational drug therapy. Eur J Clin Pharmacol 64(2):183–199

    Article  Google Scholar 

  6. Trifiro G, Spina E (2011) Age-related changes in pharmacodynamics: focus on drugs acting on central nervous and cardiovascular systems. Curr Drug Metab 12(7):611–620

    Article  CAS  Google Scholar 

  7. Masoro EJ (1988) Physiological system markers of aging. Exp Gerontol 23(4–5):391–394

    Article  CAS  Google Scholar 

  8. NIH. Understanding the dynamics of the aging process. NIH, Bethesda

  9. Strimbu K, Tavel JA (2010) What are biomarkers? Curr Opin HIV AIDS 5(6):463–466

    Article  Google Scholar 

  10. Holford N (2015) Clinical pharmacology = disease progression + drug action. Br J Clin Pharmacol 79(1):18–27

    Article  Google Scholar 

  11. Holford N (2019) Treatment response and disease progression. Transl Clin Pharmacol 27(4):123–126

    Article  Google Scholar 

  12. Peterson MC, Riggs MM (2010) A physiologically based mathematical model of integrated calcium homeostasis and bone remodeling. Bone 46(1):49–63

    Article  CAS  Google Scholar 

  13. Landersdorfer CB, Jusko WJ (2008) Pharmacokinetic/pharmacodynamic modelling in diabetes mellitus. Clin Pharmacokinet 47(7):417–448

    Article  CAS  Google Scholar 

  14. Lon HK, Liu D, Zhang Q, DuBois DC, Almon RR, Jusko WJ (2011) Pharmacokinetic-pharmacodynamic disease progression model for effect of etanercept in Lewis rats with collagen-induced arthritis. Pharm Res 28(7):1622–1630

    Article  CAS  Google Scholar 

  15. McComb M, Bies R, Ramanathan M (2021) Machine learning in pharmacometrics: opportunities and challenges. Br J Clin Pharmacol. https://doi.org/10.1111/bcp.14801

    Article  PubMed  Google Scholar 

  16. Centers for Disease Control and Prevention (CDC) (2012) Principles of epidemiology in public health practice. United States Department of Heallth and Human Services, Washington DC

    Google Scholar 

  17. Talevi A, Morales JF, Hather G, Podichetty JT, Kim S, Bloomingdale PC et al (2020) Machine learning in drug discovery and development part 1: a primer. CPT Pharmacometrics Syst Pharmacol 9(3):129–142

    Article  CAS  Google Scholar 

  18. Chaturvedula A, Calad-Thomson S, Liu C, Sale M, Gattu N, Goyal N (2019) Artificial intelligence and pharmacometrics: time to embrace, capitalize, and advance? CPT Pharmacometrics Syst Pharmacol 8(7):440–443

    Article  CAS  Google Scholar 

  19. Chow HH, Tolle KM, Roe DJ, Elsberry V, Chen H (1997) Application of neural networks to population pharmacokinetic data analysis. J Pharm Sci 86(7):840–845

    Article  CAS  Google Scholar 

  20. McComb M, Ramanathan M (2020) Generalized pharmacometric modeling, a novel paradigm for integrating machine learning algorithms: a case study of metabolomic biomarkers. Clin Pharmacol Ther 107(6):1343–1351

    Article  Google Scholar 

  21. Crimmins E, Vasunilashorn S, Kim JK, Alley D (2008) Biomarkers related to aging in human populations. Adv Clin Chem 46:161–216

    Article  CAS  Google Scholar 

  22. National Center for Health Statistics (2018) National health and nutrition survey: NHANES III (1988–1994). Centers for Disease Control and Prevention, Hyattsville

    Google Scholar 

  23. Friedewald WT, Levy RI, Fredrickson DS (1972) Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 18(6):499–502

    Article  CAS  Google Scholar 

  24. Ishwaran H, Kogalur U (2020) Fast unified random forests for survival, regression, and classification (RF-SRC). 2.9.3 ed2020. p. R package

  25. Hong S, Lynn HS (2020) Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction. BMC Med Res Methodol 20(1):199

    Article  Google Scholar 

  26. Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. MIT, Cambridge

    Google Scholar 

  27. Scutari M (2010) Learning Bayesian networks with the bnlearn R package. J Stat Softw 35(3):1–22

    Article  Google Scholar 

  28. Granger CWJ (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3):424–438

    Article  Google Scholar 

  29. Lavielle M (2018) Pharmacometrics models with hidden Markovian dynamics. J Pharmacokinet Pharmacodyn 45(1):91–105

    Article  CAS  Google Scholar 

  30. Ghahramani Z (1998) Learning dynamic Bayesian networks. In: Giles CL, Gori M (eds) Adaptive processing of sequences and data structures: international summer school on neural networks “ER Caianiello” Vietri sul Mare, Salerno, Italy, 6–13 September 1997, Tutorial Lectures. Springer, Berlin, pp 168–197

  31. Lauritzen SL (1996) Graphical models. Clarendon Press/Oxford University Press, Oxford/New York

    Google Scholar 

  32. Pearl J (2000) Causality: models, reasoning, and inference. Cambridge University Press, Cambridge

    Google Scholar 

  33. Fellows K, Stoneking CJ, Ramanathan M (2015) Bayesian population modeling of drug dosing adherence. J Pharmacokinet Pharmacodyn 42(5):515–525

    Article  CAS  Google Scholar 

  34. Knights J, Heidary Z, Peters-Strickland T, Ramanathan M (2019) Evaluating digital medicine ingestion data from seriously mentally ill patients with a Bayesian Hybrid Model. NPJ Digit Med 2:20

    Article  Google Scholar 

  35. Sims CA, Stock JH, Watson MW (1990) Inference in linear time series models with some unit roots. Econometrica 58(1):113–144

    Article  Google Scholar 

  36. Engle RF, Granger CWJ (1987) Co-integration and error correction: representation, estimation and testing. Econometrica 55(2):251–276

    Article  Google Scholar 

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Acknowledgements

Funding for the Ramanathan laboratory from MS190096 from Department of Defense Congressionally Directed Medical Research Programs, USAMRDC, Multiple Sclerosis Research Program and from NIGMS 131800 (PI: William Jusko) is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences, University at Buffalo, The State University of New York, 355 Pharmacy Building, Buffalo, NY, 14214-8033, USA

    Mason McComb & Murali Ramanathan

  2. Department of Biostatistics, University at Buffalo, The State University of New York, Buffalo, NY, USA

    Rachael Hageman Blair

  3. Department of Biostatistics, University of Waterloo, Waterloo, ON, Canada

    Martin Lysy

  4. Department of Neurology, University at Buffalo, The State University of New York, Buffalo, NY, USA

    Murali Ramanathan

Authors
  1. Mason McComb
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  2. Rachael Hageman Blair
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  3. Martin Lysy
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  4. Murali Ramanathan
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Contributions

MM—performed research, analyzed data, wrote manuscript. RHB—data interpretation, Bayesian networks, manuscript preparation. ML—stochastic modeling, manuscript preparation. MR—study concept and design, data analysis and interpretation, manuscript preparation.

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Correspondence to Murali Ramanathan.

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Conflict of interest:

Mason McComb has no conflicts to disclose. Rachael Hageman Blair received funding from Department of Defense. Martin Lysy has no conflicts to disclose. Murali Ramanathan received research funding from the National Science Foundation, Department of Defense and Otsuka Pharmaceuticals and the National Institutes of Health. These are unrelated to the research presented in this report.

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McComb, M., Blair, R.H., Lysy, M. et al. Machine learning-guided, big data-enabled, biomarker-based systems pharmacology: modeling the stochasticity of natural history and disease progression. J Pharmacokinet Pharmacodyn 49, 65–79 (2022). https://doi.org/10.1007/s10928-021-09786-5

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  • DOI: https://doi.org/10.1007/s10928-021-09786-5

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