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1.  A Priori Prediction of Tumor Payload Concentrations: Preclinical Case Study with an Auristatin-Based Anti-5T4 Antibody-Drug Conjugate 
The AAPS Journal  2014;16(3):452-463.
The objectives of this investigation were as follows: (a) to validate a mechanism-based pharmacokinetic (PK) model of ADC for its ability to a priori predict tumor concentrations of ADC and released payload, using anti-5T4 ADC A1mcMMAF, and (b) to analyze the PK model to find out main pathways and parameters model outputs are most sensitive to. Experiential data containing biomeasures, and plasma and tumor concentrations of ADC and payload, following A1mcMMAF administration in two different xenografts, were used to build and validate the model. The model performed reasonably well in terms of a priori predicting tumor exposure of total antibody, ADC, and released payload, and the exposure of released payload in plasma. Model predictions were within two fold of the observed exposures. Pathway analysis and local sensitivity analysis were conducted to investigate main pathways and set of parameters the model outputs are most sensitive to. It was discovered that payload dissociation from ADC and tumor size were important determinants of plasma and tumor payload exposure. It was also found that the sensitivity of the model output to certain parameters is dose-dependent, suggesting caution before generalizing the results from the sensitivity analysis. Model analysis also revealed the importance of understanding and quantifying the processes responsible for ADC and payload disposition within tumor cell, as tumor concentrations were sensitive to these parameters. Proposed ADC PK model provides a useful tool for a priori predicting tumor payload concentrations of novel ADCs preclinically, and possibly translating them to the clinic.
Electronic supplementary material
The online version of this article (doi:10.1208/s12248-014-9576-9) contains supplementary material, which is available to authorized users.
PMCID: PMC4012047  PMID: 24578215
antibody–drug conjugate; pharmacokinetic modeling; preclinical-to-clinical translation; sensitivity analysis; tumor drug disposition
2.  Pharmacodynamic Model of Sodium–Glucose Transporter 2 (SGLT2) Inhibition: Implications for Quantitative Translational Pharmacology 
The AAPS Journal  2011;13(4):576-584.
Sodium–glucose co-transporter-2 (SGLT2) inhibitors are an emerging class of agents for use in the treatment of type 2 diabetes mellitus (T2DM). Inhibition of SGLT2 leads to improved glycemic control through increased urinary glucose excretion (UGE). In this study, a biologically based pharmacokinetic/pharmacodynamic (PK/PD) model of SGLT2 inhibitor-mediated UGE was developed. The derived model was used to characterize the acute PK/PD relationship of the SGLT2 inhibitor, dapagliflozin, in rats. The quantitative translational pharmacology of dapagliflozin was examined through both prospective simulation and direct modeling of mean literature data obtained for dapagliflozin in healthy subjects. Prospective simulations provided time courses of UGE that were of consistent shape to clinical observations, but were modestly biased toward under prediction. Direct modeling provided an improved characterization of the data and precise parameter estimates which were reasonably consistent with those predicted from preclinical data. Overall, these results indicate that the acute clinical pharmacology of SGLT2 inhibitors in healthy subjects can be reasonably well predicted from preclinical data through rational accounting of species differences in pharmacokinetics, physiology, and SGLT2 pharmacology. Because these data can be generated at the earliest stages of drug discovery, the proposed model is useful in the design and development of novel SGLT2 inhibitors. In addition, this model is expected to serve as a useful foundation for future efforts to understand and predict the effects of SGLT2 inhibition under chronic administration and in other patient populations.
PMCID: PMC3231856  PMID: 21870203
diabetes; glucosuria; pharmacodynamics; pharmacokinetics; SGLT2; translational pharmacology; UGE
3.  The role of multiscale computational approaches for rational design of conventional and nanoparticle oral drug delivery systems 
Multiscale computational modeling of drug delivery systems (DDS) is poised to provide predictive capabilities for the rational design of targeted drug delivery systems, including multi-functional nanoparticles. Realistic, mechanistic models can provide a framework for understanding the fundamental physico-chemical interactions between drug, delivery system, and patient. Multiscale computational modeling, however, is in its infancy even for conventional drug delivery. The wide range of emerging nanotechnology systems for targeted delivery further increases the need for reliable in silico predictions. This review will present existing computational approaches at different scales in the design of traditional oral drug delivery systems. Subsequently, a multiscale framework for integrating continuum, stochastic, and computational chemistry models will be proposed and a case study will be presented for conventional DDS. The extension of this framework to emerging nanotechnology delivery systems will be discussed along with future directions. While oral delivery is the focus of the review, the outlined computational approaches can be applied to other drug delivery systems as well.
PMCID: PMC2676650  PMID: 18019831
Oral drug delivery; multiscale; computational modeling; continuum; computational chemistry; stochastic

Results 1-3 (3)