Big data to smart data in Alzheimer's disease: Real-world examples of advanced modeling and simulation

Alzheimer's and Dementia

Volumn 12, Issue 9, 2016, Pages 1022-1030

  Haas, Magali ^a   Stephenson, Diane ^b   Romero, Klaus ^b   Gordon, Mark Forrest ^c   Zach, Neta ^d   Geerts, Hugo ^e  

^a Orion Bionetworks   (United States)

^b CRITICAL PATH INSTITUTE   (United States)

^c BOEHRINGER INGELHEIM PHARMACEUTICALS INC   (United States)

^d Prize4Life   (Israel)

^e In Silico Biosciences   (United States)

Author keywords

Alzheimer's dementia; Brain disorders; Crowdsourcing; Private public partnership; Quantitative systems pharmacology; Regulatory

Indexed keywords

ALZHEIMER DISEASE; BIOINFORMATICS; CLINICAL OUTCOME; CLINICAL PATHWAY; COMPUTER MODEL; COMPUTER SIMULATION; CROWDSOURCING; DATA PROCESSING; DEGENERATIVE DISEASE; HUMAN; MATHEMATICAL ANALYSIS; MEDICAL DECISION MAKING; MULTIPLE SCLEROSIS; NON PROFIT ORGANIZATION; NONHUMAN; PHENOTYPE; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; PUBLIC-PRIVATE PARTNERSHIP; QUANTITATIVE SYSTEMS PHARMACOLOGY; REVIEW; SCHIZOPHRENIA; ANIMAL; BIOLOGICAL MODEL; DRUG DEVELOPMENT; FACTUAL DATABASE; PATHOPHYSIOLOGY; PROCEDURES;

ALZHEIMER DISEASE; ANIMALS; COMPUTER SIMULATION; CROWDSOURCING; DATABASES, FACTUAL; DRUG DISCOVERY; HUMANS; MODELS, NEUROLOGICAL; MULTIPLE SCLEROSIS; PUBLIC-PRIVATE SECTOR PARTNERSHIPS; SCHIZOPHRENIA;

EID: 84990997653     PISSN: 15525260     EISSN: 15525279     Source Type: Journal    
DOI: 10.1016/j.jalz.2016.05.005     Document Type: Review

References (43)

1. [1] Zahs, K.R., Ashe, K.H., ‘Too much good news’ - are Alzheimer mouse models trying to tell us how to prevent, not cure, Alzheimer's disease?. Trends Neurosci 33 (2010), 381–389.
2. [2] Snyder, H.M., Bain, L.J., Egge, R., Carrillo, M.C., Alzheimer's disease public-private partnerships: A landscape of the global nonprofit community. Alzheimers Dement 9 (2013), 466–471.
3. [3] Brumfield, M., The Critical Path Institute: transforming competitors into collaborators. Nat Rev Drug Discov 13 (2014), 785–786.
4. [4] Romero, K., de Mars, M., Frank, D., Anthony, M., Neville, J., Kirby, L., et al. The Coalition Against Major Diseases: developing tools for an integrated drug development process for Alzheimer's and Parkinson's diseases. Clin Pharmacol Ther 86 (2009), 365–367.
5. [5] Neville, J., Kopko, S., Broadbent, S., Aviles, E., Stafford, R., Solinsky, C.M., et al. Development of a unified clinical trial database for Alzheimer's disease. Alzheimers Dement 11 (2015), 1212–1221.
6. [6] Rogers, J.A., Polhamus, D., Gillespie, W.R., Ito, K., Romero, K., Qiu, R., et al. Combining patient-level and summary-level data for Alzheimer's disease modeling and simulation: a beta regression meta-analysis. J Pharmacokinet Pharmacodyn 39 (2012), 479–498.
7. [7] Romero, K., Sinha, V., Allerheiligen, S., Danhof, M., Pinheiro, J., Kruhlak, N., et al. Modeling and simulation for medical product development and evaluation: highlights from the FDA-C-Path-ISOP 2013 workshop. J Pharmacokinet Pharmacodyn 41 (2014), 545–552.
8. [8] Ramagopalan, S.V., Sadovnick, A.D., Epidemiology of multiple sclerosis. Neurol Clin 29 (2011), 207–217.
9. [9] Sospedra, M., Martin, R., Immunology of multiple sclerosis. Annu Rev Immunol 23 (2005), 683–747.
10. [10] Mahad, D.H., Trapp, B.D., Lassmann, H., Pathological mechanisms in progressive multiple sclerosis. Lancet Neurol 14 (2015), 183–193.
11. [11] Comabella, M., Montalban, X., Body fluid biomarkers in multiple sclerosis. Lancet Neurol 13 (2014), 113–126.
12. [12] Bove, R., Secor, E., Healy, B.C., Musallam, A., Vaughan, T., Glanz, B.I., et al. Evaluation of an online platform for multiple sclerosis research: patient description, validation of severity scale, and exploration of BMI effects on disease course. PLoS One, 8, 2013, e59707.
13. [13] Gauthier, S.A., Glanz, B.I., Mandel, M., Weiner, H.L., A model for the comprehensive investigation of a chronic autoimmune disease: the multiple sclerosis CLIMB study. Autoimmun Rev 5 (2006), 532–536.
14. [14] Xing, H., McDonagh, P.D., Bienkowska, J., Cashorali, T., Runge, K., Miller, R.E., et al. Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis. PLoS Comput Biol, 7, 2011, e1001105.
15. [15] Lee, E., Chuang, H.Y., Kim, J.W., Ideker, T., Lee, D., Inferring pathway activity toward precise disease classification. PLoS Comput Biol, 4, 2008, e1000217.
16. [16] Su, J., Yoon, B.J., Dougherty, E.R., Accurate and reliable cancer classification based on probabilistic inference of pathway activity. PLoS One, 4, 2009, e8161.
17. [17] Johannes, M., Brase, J.C., Frohlich, H., Gade, S., Gehrmann, M., Falth, M., et al. Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients. Bioinformatics 26 (2010), 2136–2144.
18. [18] Chuang, H.Y., Rassenti, L., Salcedo, M., Licon, K., Kohlmann, A., Haferlach, T., et al. Subnetwork-based analysis of chronic lymphocytic leukemia identifies pathways that associate with disease progression. Blood 120 (2012), 2639–2649.
19. [19] Kim, S., Kon, M., DeLisi, C., Pathway-based classification of cancer subtypes. Biol Direct, 7, 2012, 21.
20. [20] Zhang, M., Zhang, L., Zou, J., Yao, C., Xiao, H., Liu, Q., et al. Evaluating reproducibility of differential expression discoveries in microarray studies by considering correlated molecular changes. Bioinformatics 25 (2009), 1662–1668.
21. [21] Hofree, M., Shen, J.P., Carter, H., Gross, A., Ideker, T., Network-based stratification of tumor mutations. Nat Methods 10 (2013), 1108–1115.
22. [22] International Multiple Sclerosis Genetics Consortium. Network-based multiple sclerosis pathway analysis with GWAS data from 15,000 cases and 30,000 controls. Am J Hum Genet 92 (2013), 854–865.
23. [23] Athey, B.D., Braxenthaler, M., Haas, M., Guo, Y., tranSMART: An open source and community-driven informatics and data sharing platform for clinical and translational research. AMIA Jt Summits Transl Sci Proc 2013 (2013), 6–8.
24. [24] Szalma, S., Koka, V., Khasanova, T., Perakslis, E.D., Effective knowledge management in translational medicine. J Transl Med, 8, 2010, 68.
25. [25] Geerts, H., Spiros, A., Roberts, P., Carr, R., Quantitative systems pharmacology as an extension of PK/PD modeling in CNS research and development. J Pharmacokinet Pharmacodyn 40 (2013), 257–265.
26. [26] Roberts, P.D., Spiros, A., Geerts, H., Simulations of symptomatic treatments for Alzheimer's disease: computational analysis of pathology and mechanisms of drug action. Alzheimers Res Ther, 4, 2012, 50.
27. [27] Nicholas, T., Duvvuri, S., Leurent, C., Raunig, D., Rapp, T., Iredale, R., et al. Systems pharmacology modeling in neuroscience: prediction and outcome of PF-04995274, a 5HT4 partial agonist, in a clinical scopolamine impairment trial. Adv Alzheimers Dis 2 (2013), 83–98.
28. [28] Geerts, H., Spiros, A., Roberts, P., Twyman, R., Alphs, L., Grace, A.A., Blinded prospective evaluation of computer-based mechanistic schizophrenia disease model for predicting drug response. PLoS One, 7, 2012, e49732.
29. [29] Liu, J., Ogden, A., Comery, T.A., Spiros, A., Roberts, P., Geerts, H., Prediction of efficacy of vabicaserin, a 5-ht2c agonist, for the treatment of schizophrenia using a quantitative systems pharmacology model. CPT Pharmacometrics Syst Pharmacol, 3, 2014, e111.
30. [30] Geerts, H., Roberts, P., Spiros, A., Potkin, S., Understanding responder neurobiology in schizophrenia using a quantitative systems pharmacology model: Application to iloperidone. J Psychopharmacol 29 (2015), 372–382.
31. [31] Lavedan, C., Licamele, L., Volpi, S., Hamilton, J., Heaton, C., Mack, K., et al. Association of the NPAS3 gene and five other loci with response to the antipsychotic iloperidone identified in a whole genome association study. Mol Psychiatry 14 (2009), 804–819.
32. [32] Geerts, H., Spiros, A., Roberts, P., Assessing the synergy between cholinomimetics and memantine as augmentation therapy in cognitive impairment in schizophrenia. A virtual human patient trial using quantitative systems pharmacology. Front Pharmacol, 6, 2015, 198.
33. [33] Kiernan, M.C., Vucic, S., Cheah, B.C., Turner, M.R., Eisen, A., Hardiman, O., et al. Amyotrophic lateral sclerosis. Lancet 377 (2011), 942–955.
34. [34] Cutter, G.R., Baier, M.L., Rudick, R.A., Cookfair, D.L., Fischer, J.S., Petkau, J., et al. Development of a multiple sclerosis functional composite as a clinical trial outcome measure. Brain 122 (1999), 871–882.
35. [35] Ontaneda, D., LaRocca, N., Coetzee, T., Rudick, R., Revisiting the multiple sclerosis functional composite: proceedings from the National Multiple Sclerosis Society (NMSS) Task Force on Clinical Disability Measures. Mult Scler 18 (2012), 1074–1080.
36. [36] Atassi, N., Berry, J., Shui, A., Zach, N., Sherman, A., Sinani, E., et al. The PRO-ACT database: design, initial analyses, and predictive features. Neurology 83 (2014), 1719–1725.
37. [37] Marbach, D., Costello, J.C., Kuffner, R., Vega, N.M., Prill, R.J., Camacho, D.M., et al. Wisdom of crowds for robust gene network inference. Nat Methods 9 (2012), 796–804.
38. [38] Kuffner, R., Zach, N., Norel, R., Hawe, J., Schoenfeld, D., Wang, L., et al. Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression. Nat Biotechnol 33 (2015), 51–57.
39. [39] Miller, R., Ewy, W., Corrigan, B.W., Ouellet, D., Hermann, D., Kowalski, K.G., et al. How modeling and simulation have enhanced decision making in new drug development. J Pharmacokinet Pharmacodyn 32 (2005), 185–197.
40. [40] Bockbrader, H.N., Wesche, D., Miller, R., Chapel, S., Janiczek, N., Burger, P., A comparison of the pharmacokinetics and pharmacodynamics of pregabalin and gabapentin. Clin Pharmacokinet 49 (2010), 661–669.
41. [41] Ahmad, A., Garnett, W.R., Simulated fluctuations in plasma drug concentrations for patients receiving oxcarbazepine or carbamazepine extended-release capsules. Clin Drug Investig 25 (2005), 669–673.
42. [42] Eun, S.H., Kim, H.D., Chung, H.J., Kang, H.C., Lee, J.S., Kim, J.S., et al. A multicenter trial of oxcarbazepine oral suspension monotherapy in children newly diagnosed with partial seizures: a clinical and cognitive evaluation. Seizure 21 (2012), 679–684.
43. [43] Peterson, M.C., Riggs, M.M., FDA advisory meeting clinical pharmacology review utilizes a quantitative systems pharmacology (QSP) model: a watershed moment?. CPT Pharmacometrics Syst Pharmacol, 4, 2015, e00020.