269A case-control study of Japanese cancer patients revealed that those with the
variant UGT1A1 alleles were at signifi cantly higher risk of severe adverse reactions
to irinotecan (Ando et al. 2005 ). However, fi ndings of subsequent irinotecan phar-
macogenetic studies have been inconsistent. In a metaanalysis, data presented in
nine studies that included a total of 10 sets of patients was reviewed for assessment
of the association of irinotecan dose with the risk of irinotecan-related hematologic
toxicities for patients with a UGT1A128/28 genotype (Hoskins et al. 2007 ). The
risk of toxicity was higher among patients with a UGT1A128/28 genotype than
among those with a UGT1A11/1 or UGT1A11/28 genotype at both medium
and high doses of irinotecan, but risk was similar at lower doses. The risk of expe-
riencing irinotecan-induced hematologic toxicity for patients with a UGT1A128/28
genotype thus appears to be a function of the dose of irinotecan administered.
Role of Computational Models in Personalized
Anticancer Therapy
A Computational Model of Kinetically Tailored Treatment
Histological characteristics of a tumor are not a reliable indicator the natural history.
Mechanism-based framework using cDNA arrays and computational models have
promise in improving diagnosis and prediction, and thereby making tailored ther-
apy possible. Treatment strategies may be tailored to individuals based on tumor
cell kinetics. Computational models of kinetically tailored treatment have been
developed to predict drug combinations, doses, and schedules likely to be effective
in reducing tumor size and prolonging patient life. Such models incorporate intratu-
mor heterogeneity as well as evolution of drug resistance, apoptotic rates, and cell
division rates. These models may predict how combination chemotherapy of cell-
cycle phase-specifi c, phase-non-specifi c, and cytostatic drugs affect tumor growth
and evolution. Additional tests of the model are needed in which physicians collect
information on apoptotic and proliferative indices, cell-cycle times, and drug resis-
tance from biopsies of each individual’s tumor. Computational models may become
important tools to help optimize and tailor cancer treatments. Ideal characteristics
of anticancer drug development suitable for personalized approach are:
- Designed to inhibit specifi c biologic pathways involved in oncogenesis
- Mechanistic specifi city rather than organ/tissue selectivity
- Should fi t with initiatives in individualized therapy: cDNA arrays and
computational models - Synergistic with other chemotherapeutic agents
- Prevent or delay the emergence of resistance
- Transform cancer into a chronic disease by delaying time-to-progression
Role of Computational Models in Personalized Anticancer Therapy