Revitalizing Personalized Medicine: Respecting Biomolecular Complexities Beyond Gene Expression

Devaraj, Jayachandran; Ramkrishna, U.; Skiles, Jodi; Renbarger, Jamie L.; Ramkrishna, Doraiswami

doi:10.1038/psp.2014.6

Cited by 6 publications

(7 citation statements)

References 74 publications

(104 reference statements)

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“…Analysis of patient’s biological fluids by Mass Spectrometry (MS) or NMR spectroscopy helps identify pre-drug metabolomics signatures that can predict the post-drug exposure effects and provides molecular basis for variability in drug response. Metabolomics captures complex aspects of human biology, reflective of individual genomics and environmental exposures and, together with direct pharmacogenomic approaches, brings closer the prospect of precision medicine [84,85]. …”

Section: Pharmacometabolomicsmentioning

confidence: 99%

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Mazaleuskaya

Sangkuhl²,

Thorn³

et al. 2015

Pharmacogenetics and Genomics

282

141

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Section: Pharmacometabolomicsmentioning

confidence: 99%

PharmGKB summary

Mazaleuskaya

Sangkuhl²,

Thorn³

et al. 2015

Pharmacogenetics and Genomics

282

141

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“…The predictive capacity of PBPK models allows them to also be successfully applied in assessing impacts on dose and particle size as well as on the in vivo performance of nanoformulations, either for medicines under development or for marketed drugs (148). However, fundamental issues exist that are hampering the advancement of personalized medicine and that highlight the lack of emerging predictive tools that could serve as a decision support mechanism for physicians to personalize treatment (149,150). Particularly, this seems even more difficult for drug delivery by attempting to unify knowl-edge from heterogeneous data derived e.g.…”

Section: Future Perspectives In Advancing Pbpk Models To Ensure Practmentioning

confidence: 99%

Enabling personalized cancer medicine decisions: The challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics (Review)

et al. 2016

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The existing tumor heterogeneity and the complexity of cancer cell biology critically demand powerful translational tools with which to support interdisciplinary efforts aiming to advance personalized cancer medicine decisions in drug development and clinical practice. The development of physiologically based pharmacokinetic (PBPK) models to predict the effects of drugs in the body facilitates the clinical translation of genomic knowledge and the implementation of in vivo pharmacology experience with pharmacogenomics. Such a direction unequivocally empowers our capacity to also make personalized drug dosage scheme decisions for drugs, including molecularly targeted agents and innovative nanoformulations, i.e. in establishing pharmacotyping in prescription. In this way, the applicability of PBPK models to guide individualized cancer therapeutic decisions of broad clinical utility in nanomedicine in real-time and in a cost-affordable manner will be discussed. The latter will be presented by emphasizing the need for combined efforts within the scientific borderlines of genomics with nanotechnology to ensure major benefits and productivity for nanomedicine and personalized medicine interventions.

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“…Although pharmacogenomics provides some information vital for predicting treatment outcome in the subgroups of patient population, its ability to explain within-group variation may be in question. A detailed account this subject can be found in [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization

et al. 2015

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6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP’s widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient’s ability to metabolize the drug instead of the traditional standard-dose-for-all approach.

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Revitalizing Personalized Medicine: Respecting Biomolecular Complexities Beyond Gene Expression

Cited by 6 publications

References 74 publications

PharmGKB summary

PharmGKB summary

Enabling personalized cancer medicine decisions: The challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics (Review)

Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization

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