Quantitative Translational Modeling to Facilitate Preclinical to Clinical Efficacy & Toxicity Translation in Oncology

References

• Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 144(5), 646–674 (2011).
   PubMed Web of Science ®Google Scholar
• Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 100(1), 57–70 (2000).
   PubMed Web of Science ®Google Scholar
• Chen DS, Mellman I. Oncology meets immunology: the cancer-immunity cycle. Immunity 39(1), 1–10 (2013).
   PubMed Web of Science ®Google Scholar
• Mullard A. Parsing clinical success rates. Nat. Rev. Drug Discov. 15(7), 447 (2016).
   Web of Science ®Google Scholar
• Ledford H. Translational research: 4 ways to fix the clinical trial. Nature 477(7366), 526–528 (2011).
   PubMed Web of Science ®Google Scholar
• Venkatakrishnan K, Friberg LE, Ouellet D et al. Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities. Clin. Pharmacol. Ther. 97(1), 37–54 (2015).
   PubMed Web of Science ®Google Scholar
• Sharma MR, Maitland ML, Ratain MJ. Models of excellence: improving oncology drug development. Clin. Pharmacol. Ther. 92(5), 548–550 (2012).
   PubMed Web of Science ®Google Scholar
• US Department of Health and Human Services Food and Drug Administration. Critical path opportunities report (2006).www.fda.gov/ohrms/dockets/ac/07/briefing/2007-4329b_02_05_Critical%20Path%20Report%202006.pdf.
   Google Scholar
• Gottlieb S. How FDA plans to help consumer capitalize on advances in science (2017). https://blogs.fda.gov/fdavoice/index.php/tag/in-silico-tools/.
   Google Scholar
• Rubio-Viqueira B, Hidalgo M. Direct in vivo xenograft tumor model for predicting chemotherapeutic drug response in cancer patients. Clin. Pharmacol. Ther. 85(2), 217–221 (2009).
   PubMed Web of Science ®Google Scholar
• Jung J. Human tumor xenograft models for preclinical assessment of anticancer drug development. Toxicol. Res. 30(1), 1–5 (2014).
   PubMedGoogle Scholar
• Wong H, Choo EF, Alicke B et al. Antitumor activity of targeted and cytotoxic agents in murine subcutaneous tumor models correlates with clinical response. Clin. Cancer Res. 18(14), 3846–3855 (2012).
   PubMed Web of Science ®Google Scholar
• Sausville EA, Burger AM. Contributions of human tumor xenografts to anticancer drug development. Cancer Res. 66(7), 3351–3354; discussion 3354 (2006).
   PubMed Web of Science ®Google Scholar
• Becher OJ, Holland EC. Genetically engineered models have advantages over xenografts for preclinical studies. Cancer Res. 66(7), 3355–3358; discussion 3358–3359 (2006).
   PubMed Web of Science ®Google Scholar
• Rocchetti M, Simeoni M, Pesenti E, De Nicolao G, Poggesi I. Predicting the active doses in humans from animal studies: a novel approach in oncology. Eur. J. Cancer 43(12), 1862–1868 (2007).
   PubMed Web of Science ®Google Scholar
• Bissery MC, Guenard D, Gueritte-Voegelein F, Lavelle F. Experimental antitumor activity of taxotere (RP 56976, NSC 628503), a taxol analogue. Cancer Res. 51(18), 4845–4852 (1991).
   PubMed Web of Science ®Google Scholar
• Houghton PJ, Morton CL, Tucker C et al. The pediatric preclinical testing program: description of models and early testing results. Pediatr. Blood Cancer 49(7), 928–940 (2007).
   PubMed Web of Science ®Google Scholar
• Corbett TH, White K, Polin L et al. Discovery and preclinical antitumor efficacy evaluations of LY32262 and LY33169. Invest. New Drugs 21(1), 33–45 (2003).
   PubMed Web of Science ®Google Scholar
• Steiner P, Joynes C, Bassi R et al. Tumor growth inhibition with cetuximab and chemotherapy in non-small cell lung cancer xenografts expressing wild-type and mutated epidermal growth factor receptor. Clin. Cancer Res. 13(5), 1540–1551 (2007).
   PubMed Web of Science ®Google Scholar
• Wong H, Vernillet L, Peterson A et al. Bridging the gap between preclinical and clinical studies using pharmacokinetic–pharmacodynamic modeling: an analysis of GDC-0973, a MEK inhibitor. Clin. Cancer Res. 18(11), 3090–3099 (2012).
   PubMed Web of Science ®Google Scholar
• Hather G, Liu R, Bandi S et al. Growth rate analysis and efficient experimental design for tumor xenograft studies. Cancer Inform. 13(Suppl. 4), 65–72 (2014).
   PubMedGoogle Scholar
• Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: application of a transit compartment model to characterize methotrexate effects in vitro. AAPS PharmSci. 4(4), E42 (2002).
   PubMedGoogle Scholar
• Simeoni M, Magni P, Cammia C et al. Predictive pharmacokinetic–pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents. Cancer Res. 64(3), 1094–1101 (2004).
   PubMed Web of Science ®Google Scholar
• Bueno L, De Alwis DP, Pitou C et al. Semi-mechanistic modelling of the tumour growth inhibitory effects of LY2157299, a new type I receptor TGF-β kinase antagonist, in mice. Eur. J. Cancer 44(1), 142–150 (2008).
   PubMed Web of Science ®Google Scholar
• Jumbe NL, Xin Y, Leipold DD et al. Modeling the efficacy of trastuzumab-DM1, an antibody drug conjugate, in mice. J. Pharmacokinet. Pharmacodyn. 37(3), 221–242 (2010).
   PubMed Web of Science ®Google Scholar
• Kogame A, Tagawa Y, Shibata S et al. Pharmacokinetic and pharmacodynamic modeling of hedgehog inhibitor TAK-441 for the inhibition of Gli1 messenger RNA expression and antitumor efficacy in xenografted tumor model mice. Drug Metab. Dispos. 41(4), 727–734 (2013).
   PubMed Web of Science ®Google Scholar
• Salphati L, Wong H, Belvin M et al. Pharmacokinetic–pharmacodynamic modeling of tumor growth inhibition and biomarker modulation by the novel phosphatidylinositol 3-kinase inhibitor GDC-0941. Drug Metab. Dispos. 38(9), 1436–1442 (2010).
   PubMed Web of Science ®Google Scholar
• Yamazaki S, Skaptason J, Romero D et al. Pharmacokinetic–pharmacodynamic modeling of biomarker response and tumor growth inhibition to an orally available cMet kinase inhibitor in human tumor xenograft mouse models. Drug Metab. Dispos. 36(7), 1267–1274 (2008).
   PubMed Web of Science ®Google Scholar
• Mould DR, Walz AC, Lave T, Gibbs JP, Frame B. Developing exposure/response models for anticancer drug treatment: special considerations. CPT Pharmacometrics Syst. Pharmacol. 4(1), 12–27 (2015).
   Google Scholar
• Wong H, Belvin M, Herter S et al. Pharmacodynamics of 2-[4-[(1E)-1-(hydroxyimino)-2,3-dihydro-1H-inden-5-yl]-3-(pyridine-4-yl)-1H-pyraz ol-1-yl]ethan-1-ol (GDC-0879), a potent and selective B-Raf kinase inhibitor: understanding relationships between systemic concentrations, phosphorylated mitogen-activated protein kinase kinase 1 inhibition, and efficacy. J. Pharmacol. Exp. Ther. 329(1), 360–367 (2009).
   PubMed Web of Science ®Google Scholar
• Lombardo F, Waters NJ, Argikar UA et al. Comprehensive assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data, part 2: clearance. J. Clin. Pharmacol. 53(2), 178–191 (2013).
   PubMed Web of Science ®Google Scholar
• Lombardo F, Waters NJ, Argikar UA et al. Comprehensive assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data, part 1: volume of distribution at steady state. J. Clin. Pharmacol. 53(2), 167–177 (2013).
   PubMed Web of Science ®Google Scholar
• Margolskee A, Darwich AS, Pepin X et al. IMI – oral biopharmaceutics tools project – evaluation of bottom-up PBPK prediction success part 1: characterisation of the OrBiTo database of compounds. Eur. J. Pharm. Sci. 96, 598–609 (2017).
   PubMed Web of Science ®Google Scholar
• Li L, Gardner I, Dostalek M, Jamei M. Simulation of monoclonal antibody pharmacokinetics in humans using a minimal physiologically based model. AAPS J 16(5), 1097–1109 (2014).
   PubMed Web of Science ®Google Scholar
• Lobo ED, Hansen RJ, Balthasar JP. Antibody pharmacokinetics and pharmacodynamics. J. Pharm. Sci. 93(11), 2645–2668 (2004).
   PubMed Web of Science ®Google Scholar
• Wong H, Alicke B, West KA et al. Pharmacokinetic–pharmacodynamic analysis of vismodegib in preclinical models of mutational and ligand-dependent Hedgehog pathway activation. Clin. Cancer Res. 17(14), 4682–4692 (2011).
   PubMed Web of Science ®Google Scholar
• Tate SC, Cai S, Ajamie RT et al. Semi-mechanistic pharmacokinetic/pharmacodynamic modeling of the antitumor activity of LY2835219, a new cyclin-dependent kinase 4/6 inhibitor, in mice bearing human tumor xenografts. Clin. Cancer Res. 20(14), 3763–3774 (2014).
   PubMed Web of Science ®Google Scholar
• Palani S, Patel M, Huck J et al. Preclinical pharmacokinetic/pharmacodynamic/efficacy relationships for alisertib, an investigational small-molecule inhibitor of Aurora A kinase. Cancer Chemother. Pharmacol. 72(6), 1255–1264 (2013).
   PubMed Web of Science ®Google Scholar
• Betts AM, Haddish-Berhane N, Tolsma J et al. Preclinical to clinical translation of antibody-drug conjugates using PK/PD modeling: a retrospective analysis of inotuzumab ozogamicin. AAPS J 18(5), 1101–1116 (2016).
   PubMed Web of Science ®Google Scholar
• Chen X, Haddish-Berhane N, Moore P et al. Mechanistic projection of first in human dose for bispecific immuno-modulatory P-cadherin LP-DART – an integrated PK/PD modeling approach. Clin. Pharmacol. Ther. 3, 232–241 (2016).
   Google Scholar
• Haddish-Berhane N, Shah DK, Ma D et al. On translation of antibody–drug conjugates efficacy from mouse experimental tumors to the clinic: a PK/PD approach. J. Pharmacokinet. Pharmacodyn. 40(5), 557–571 (2013).
   PubMed Web of Science ®Google Scholar
• Sapra P, Betts A, Boni J. Preclinical and clinical pharmacokinetic/pharmacodynamic considerations for antibody–drug conjugates. Expert Rev. Clin. Pharmacol. 6(5), 541–555 (2013).
   PubMed Web of Science ®Google Scholar
• Shah DK, Haddish-Berhane N, Betts A. Bench to bedside translation of antibody–drug conjugates using a multiscale mechanistic PK/PD model: a case study with brentuximab-vedotin. J. Pharmacokinet. Pharmacodyn. 39(6), 643–659 (2012).
   PubMed Web of Science ®Google Scholar
• Singh AP, Maass KF, Betts AM et al. Evolution of antibody–drug conjugate tumor disposition model to predict preclinical tumor pharmacokinetics of Trastuzumab-Emtansine (T-DM1). AAPS J. 18(4), 861–875 (2016).
   PubMed Web of Science ®Google Scholar
• Lindauer A, Valiathan CR, Mehta K et al. Translational pharmacokinetic/pharmacodynamic modeling of tumor growth inhibition supports dose-range selection of the anti-PD-1 antibody pembrolizumab. CPT Pharmacometrics Syst. Pharmacol. 6(1), 11–20 (2017).
   PubMedGoogle Scholar
• Huck JJ, Zhang M, Mettetal J et al. Translational exposure-efficacy modeling to optimize the dose and schedule of taxanes combined with the investigational Aurora A kinase inhibitor MLN8237 (alisertib). Mol. Cancer Ther. 13(9), 2170–2183 (2014).
   PubMed Web of Science ®Google Scholar
• Terranova N, Germani M, Del Bene F, Magni P. A predictive pharmacokinetic–pharmacodynamic model of tumor growth kinetics in xenograft mice after administration of anticancer agents given in combination. Cancer Chemother. Pharmacol. 72(2), 471–482 (2013).
   PubMed Web of Science ®Google Scholar
• Choo EF, Ng CM, Berry L et al. PK–PD modeling of combination efficacy effect from administration of the MEK inhibitor GDC-0973 and PI3K inhibitor GDC-0941 in A2058 xenografts. Cancer Chemother. Pharmacol. 71(1), 133–143 (2013).
   PubMed Web of Science ®Google Scholar
• Mackey MC. Cell kinetic status of haematopoietic stem cells. Cell Prolif. 34(2), 71–83 (2001).
   PubMed Web of Science ®Google Scholar
• Pang WW, Price EA, Sahoo D et al. Human bone marrow hematopoietic stem cells are increased in frequency and myeloid-biased with age. Proc. Natl Acad. Sci. USA 108(50), 20012–20017 (2011).
   PubMed Web of Science ®Google Scholar
• Craig M, Humphries AR, Mackey MC. An upper bound for the half-removal time of neutrophils from circulation. Blood 128(15), 1989–1991 (2016).
   PubMed Web of Science ®Google Scholar
• Rankin SM. The bone marrow: a site of neutrophil clearance. J. Leukoc. Biol. 88(2), 241–251 (2010).
   PubMed Web of Science ®Google Scholar
• Friberg LE, Henningsson A, Maas H, Nguyen L, Karlsson MO. Model of chemotherapy-induced myelosuppression with parameter consistency across drugs. J. Clin. Oncol. 20(24), 4713–4721 (2002).
   PubMed Web of Science ®Google Scholar
• Friberg LE, Sandstrom M, Karlsson MO. Scaling the time-course of myelosuppression from rats to patients with a semi-physiological model. Invest. New Drugs 28(6), 744–753 (2010).
   PubMed Web of Science ®Google Scholar
• Zandvliet AS, Karlsson MO, Schellens JHM, Copalu W, Beijnen JH, Huitema ADR. Two-stage model-based clinical trial design to optimize Phase I development of novel anticancer agents. Invest. New Drugs 28(1), 61–75 (2009).
   PubMed Web of Science ®Google Scholar
• Soto E, Keizer RJ, Trocóniz IF et al. Predictive ability of a semi-mechanistic model for neutropenia in the development of novel anti-cancer agents: two case studies. Invest. New Drugs 29(5), 984–995 (2010).
   PubMed Web of Science ®Google Scholar
• Hansson EK, Friberg LE. The shape of the myelosuppression time profile is related to the probability of developing neutropenic fever in patients with docetaxel-induced grade IV neutropenia. Cancer Chemother. Pharmacol. 69(4), 881–890 (2012).
   PubMed Web of Science ®Google Scholar
• Pastor ML, Laffont CM, Gladieff L, Schmitt A, Chatelut E, Concordet D. Model-based approach to describe G-CSF effects in carboplatin-treated cancer patients. Pharm. Res. 30(11), 2795–2807 (2013).
   PubMed Web of Science ®Google Scholar
• Poikonen P, Saarto T, Lundin J, Joensuu H, Blomqvist C. Leucocyte nadir as a marker for chemotherapy efficacy in node-positive breast cancer treated with adjuvant CMF. Br. J. Cancer 80(11), 1763–1766 (1999).
   PubMed Web of Science ®Google Scholar
• Stein A, Voigt W, Jordan K. Chemotherapy-induced diarrhea: pathophysiology frequency and guideline-based management. Ther. Adv. Med. Oncol. 2(1), 51–63 (2010).
   PubMedGoogle Scholar
• Loriot Y, Perlemuter G, Malka D et al. Drug insight: gastrointestinal and hepatic adverse effects of molecular-targeted agents in cancer therapy. Nat. Clin. Pract. Oncol. 5(5), 268–278 (2008).
   PubMedGoogle Scholar
• Gibson RJ, Stringer AM. Chemotherapy-induced diarrhoea. Curr. Opin. Support Palliat. Care 3(1), 31–35 (2009).
   PubMedGoogle Scholar
• Benson AB, 3rd, Ajani JA, Catalano RB et al. Recommended guidelines for the treatment of cancer treatment-induced diarrhea. J. Clin. Oncol. 22(14), 2918–2926 (2004).
   PubMed Web of Science ®Google Scholar
• Gamucci T, Moscetti L, Mentuccia L et al. Optimal tolerability and high efficacy of a modified schedule of lapatinib-capecitabine in advanced breast cancer patients. J. Cancer Res. Clin. Oncol. 140(2), 221–226 (2014).
   PubMed Web of Science ®Google Scholar
• Potten CS, Loeffler M. Stem cells: attributes, cycles, spirals, pitfalls and uncertainties. Lessons for and from the crypt. Development 110(4), 1001–1020 (1990).
   PubMed Web of Science ®Google Scholar
• Bach SP, Renehan AG, Potten CS. Stem cells: the intestinal stem cell as a paradigm. Carcinogenesis 21(3), 469–476 (2000).
   PubMed Web of Science ®Google Scholar
• Nordgaard I, Mortensen PB. Digestive processes in the human colon. Nutrition 11(1), 37–45 (1995).
   PubMed Web of Science ®Google Scholar
• Paulus U, Potten CS, Loeffler M. A model of the control of cellular regeneration in the intestinal crypt after perturbation based solely on local stem cell regulation. Cell Prolif. 25(6), 559–578 (1992).
   PubMed Web of Science ®Google Scholar
• Carulli AJ, Samuelson LC, Schnell S. Unraveling intestinal stem cell behavior with models of crypt dynamics. Integr. Biol. (Camb.) 6(3), 243–257 (2014).
   PubMed Web of Science ®Google Scholar
• Tomlinson IP, Bodmer WF. Failure of programmed cell death and differentiation as causes of tumors: some simple mathematical models. Proc. Natl Acad. Sci. USA 92(24), 11130–11134 (1995).
   PubMed Web of Science ®Google Scholar
• Itzkovitz S, Blat IC, Jacks T, Clevers H, Van Oudenaarden A. Optimality in the development of intestinal crypts. Cell 148(3), 608–619 (2012).
   PubMed Web of Science ®Google Scholar
• Van Leeuwen IM, Mirams GR, Walter A et al. An integrative computational model for intestinal tissue renewal. Cell Prolif. 42(5), 617–636 (2009).
   PubMed Web of Science ®Google Scholar
• Shankaran H, Cronin A, Barnes J et al. Systems pharmacology model of gastrointestinal damage predicts species differences and optimizes clinical dosing schedules. CPT Pharmacometrics Syst. Pharmacol. 7(1), 26–33 (2017).
   PubMedGoogle Scholar
• Hecht JR. Gastrointestinal toxicity or irinotecan. Oncology (Williston Park) 12(8 Suppl. 6), 72–78 (1998).
   PubMed Web of Science ®Google Scholar
• Gintant G, Sager PT, Stockbridge N. Evolution of strategies to improve preclinical cardiac safety testing. Nat. Rev. Drug Discov. 15(7), 457–471 (2016).
   PubMed Web of Science ®Google Scholar
• Wolenski FS, Zhu AZ, Johnson M et al. Fasiglifam (TAK-875) alters bile acid homeostasis in rats and dogs: a potential cause of drug induced liver injury. Toxicol. Sci. 157(1), 50–61 (2017).
   PubMed Web of Science ®Google Scholar
• Sturla SJ, Boobis AR, Fitzgerald RE et al. Systems toxicology: from basic research to risk assessment. Chem. Res. Toxicol. 27(3), 314–329 (2014).
   PubMed Web of Science ®Google Scholar
• Gao H, Korn JM, Ferretti S et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21(11), 1318–1325 (2015).
   PubMed Web of Science ®Google Scholar
• Dienstmann R, Vermeulen L, Guinney J, Kopetz S, Tejpar S, Tabernero J. Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer. Nat. Rev. Cancer 17(2), 79–92 (2017).
   PubMed Web of Science ®Google Scholar
• Guinney J, Dienstmann R, Wang X et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 21(11), 1350–1356 (2015).
   PubMed Web of Science ®Google Scholar