Skip to main content
Erschienen in: Cancer Chemotherapy and Pharmacology 1/2022

17.11.2021 | Original Article

A translational model-based approach to inform the choice of the dose in phase 1 oncology trials: the case study of erdafitinib

verfasst von: E. M. Tosca, N. Terranova, K. Stuyckens, A. G. Dosne, T. Perera, J. Vialard, P. King, T. Verhulst, J. J. Perez-Ruixo, P. Magni, I. Poggesi

Erschienen in: Cancer Chemotherapy and Pharmacology | Ausgabe 1/2022

Einloggen, um Zugang zu erhalten

Abstract

Purpose

Erdafitinib (JNJ-42756493, BALVERSA) is a tyrosine kinase inhibitor indicated for the treatment of advanced urothelial carcinoma. In this work, a translational model-based approach to inform the choice of the doses in phase 1 trials is illustrated.

Methods

A pharmacokinetic (PK) model was developed to describe the time course of erdafitinib plasma concentrations in mice and rats. Data from multiple xenograft studies in mice and rats were analyzed using the Simeoni tumor growth inhibition (TGI) model. The model parameters were used to derive a range of erdafitinib exposures that might inform the choice of the doses in oncology phase 1 trials. Conversion of exposures to doses was based on preliminary PK assessments from the first-in human (FIH) study.

Results

A one-compartment PK disposition model, with linear absorption and dose-dependent clearance, adequately described the PK data in both mice and rats via an allometric scaling approach. The TGI model was able to describe tumor growth dynamics, providing quantitative measurements of erdafitinib antitumor potency in mice and rats. Based on these estimates, ranges of efficacious unbound concentration were identified for erdafitinib in mice (0.642–5.364 μg/L) and rats (0.782–2.565 μg/L). Based on the FIH data, it was possible to transpose exposures into doses and doses of above 4 mg/day provided erdafitinib exposures associated with significant TGI in animals. The findings were in agreement with the results of the FIH trial, in which the first hints of clinical activities were observed at 6 mg.

Conclusion

The successful modeling exercise of erdafitinib preclinical data showed how translational PK-PD modeling might be a tool to help to inform the choice of the doses in FIH studies.
Anhänge
Nur mit Berechtigung zugänglich
Literatur
1.
Zurück zum Zitat DiMasi JA, Grabowski HG (2007) Economics of new oncology drug development. J Clin Oncol 25(2):209–216CrossRef DiMasi JA, Grabowski HG (2007) Economics of new oncology drug development. J Clin Oncol 25(2):209–216CrossRef
2.
Zurück zum Zitat Adams CP, Brantner VV (2006) Estimating the cost of new drug development: is it really 802 million dollars? Health Aff 25(2):420–428CrossRef Adams CP, Brantner VV (2006) Estimating the cost of new drug development: is it really 802 million dollars? Health Aff 25(2):420–428CrossRef
3.
Zurück zum Zitat Bonate PL (2011) Modeling tumor growth in oncology. Pharmacokinetics in drug development. Springer, Berlin, pp 1–19CrossRef Bonate PL (2011) Modeling tumor growth in oncology. Pharmacokinetics in drug development. Springer, Berlin, pp 1–19CrossRef
4.
Zurück zum Zitat Wang Z, Deisboeck TS (2014) Mathematical modeling in cancer drug discovery. Drug Discov Today 19(2):145–150CrossRef Wang Z, Deisboeck TS (2014) Mathematical modeling in cancer drug discovery. Drug Discov Today 19(2):145–150CrossRef
5.
Zurück zum Zitat Zhang P, Brusic V (2014) Mathematical modeling for novel cancer drug discovery and development. Expert Opin Drug Discov 9(10):1133–1150CrossRef Zhang P, Brusic V (2014) Mathematical modeling for novel cancer drug discovery and development. Expert Opin Drug Discov 9(10):1133–1150CrossRef
6.
Zurück zum Zitat Carrara L, Lavezzi SM, Borella E, De Nicolao G, Magni P, Poggesi I (2017) Current mathematical models for cancer drug discovery. Expert Opin Drug Discov 12(8):785–799PubMed Carrara L, Lavezzi SM, Borella E, De Nicolao G, Magni P, Poggesi I (2017) Current mathematical models for cancer drug discovery. Expert Opin Drug Discov 12(8):785–799PubMed
7.
Zurück zum Zitat Lobo ED, Balthasar JP (2002) Pharmacodynamic modeling of chemotherapeutic effects: application of a transit compartment model to characterize methotrexate effects in vitro. AAPS PharmSci 4(4):212–222CrossRef Lobo ED, Balthasar JP (2002) Pharmacodynamic modeling of chemotherapeutic effects: application of a transit compartment model to characterize methotrexate effects in vitro. AAPS PharmSci 4(4):212–222CrossRef
8.
Zurück zum Zitat Hahnfeldt P, Panigrahy D, Folkman J, Hlatky L (1999) Tumor development under angiogenic signaling: a dynamical theory of tumor growth, treatment response, and postvascular dormancy. Cancer Res 59(19):4770–4775PubMed Hahnfeldt P, Panigrahy D, Folkman J, Hlatky L (1999) Tumor development under angiogenic signaling: a dynamical theory of tumor growth, treatment response, and postvascular dormancy. Cancer Res 59(19):4770–4775PubMed
9.
Zurück zum Zitat Simeoni M, Magni P, Cammia C, De Nicolao G, Croci V, Pesenti E et al (2004) Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents. Cancer Res 64(3):1094–1101CrossRef Simeoni M, Magni P, Cammia C, De Nicolao G, Croci V, Pesenti E et al (2004) Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents. Cancer Res 64(3):1094–1101CrossRef
10.
Zurück zum Zitat Tosca EM, Rocchetti M, Pesenti E, Magni P (2020) A tumor-in-host DEB-based approach for modeling cachexia and bevacizumab resistance. Cancer Res 80(4):820–831CrossRef Tosca EM, Rocchetti M, Pesenti E, Magni P (2020) A tumor-in-host DEB-based approach for modeling cachexia and bevacizumab resistance. Cancer Res 80(4):820–831CrossRef
11.
Zurück zum Zitat Tosca EM, Pigatto MC, Dalla Costa T, Magni P (2019) A population dynamic energy budget-based tumor growth inhibition model for etoposide effects on Wistar rats. Pharm Res 36(3):38CrossRef Tosca EM, Pigatto MC, Dalla Costa T, Magni P (2019) A population dynamic energy budget-based tumor growth inhibition model for etoposide effects on Wistar rats. Pharm Res 36(3):38CrossRef
12.
Zurück zum Zitat Terranova N, Germani M, Del Bene F, Magni P (2013) 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):471–482CrossRef Terranova N, Germani M, Del Bene F, Magni P (2013) 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):471–482CrossRef
13.
Zurück zum Zitat Terranova N, Tosca EM, Pesenti E, Rocchetti M, Magni P (2018) Modeling tumor growth inhibition and toxicity outcome after administration of anticancer agents in xenograft mice: a dynamic energy budget (DEB) approach. J Theor Biol 450:1–14CrossRef Terranova N, Tosca EM, Pesenti E, Rocchetti M, Magni P (2018) Modeling tumor growth inhibition and toxicity outcome after administration of anticancer agents in xenograft mice: a dynamic energy budget (DEB) approach. J Theor Biol 450:1–14CrossRef
14.
Zurück zum Zitat Tosca EM, Rocchetti M, Magni P (2021) A Dynamic Energy Budget (DEB) based modeling framework to describe tumor-in-host growth inhibition and cachexia onset during anticancer treatment in in vivo xenograft studies. Oncotarget 12(14):1434CrossRef Tosca EM, Rocchetti M, Magni P (2021) A Dynamic Energy Budget (DEB) based modeling framework to describe tumor-in-host growth inhibition and cachexia onset during anticancer treatment in in vivo xenograft studies. Oncotarget 12(14):1434CrossRef
15.
Zurück zum Zitat Tosca EM, Rocchetti M, Pesenti E, Magni P (2018) Modeling resistance development to Bevacizumab in xenograft experiments by coupling hypoxia-mediated mechanism and a Dynamic Energy Budget (DEB) based tumor-in-host model. J Pharmacokinet Pharmacodyn 45:S25–S26 Tosca EM, Rocchetti M, Pesenti E, Magni P (2018) Modeling resistance development to Bevacizumab in xenograft experiments by coupling hypoxia-mediated mechanism and a Dynamic Energy Budget (DEB) based tumor-in-host model. J Pharmacokinet Pharmacodyn 45:S25–S26
16.
Zurück zum Zitat Tosca EM, Gauderat G, Fouliard S, Burbridge M, Chenel M, Magni P (2021) Modeling restoration of gefitinib efficacy by co‐administration of MET inhibitors in an EGFR inhibitor‐resistant NSCLC xenograft model: A tumor‐in‐host DEB‐based approach. CPT pharmacometrics Syst Pharmacol 1–16. https://doi.org/10.1002/psp4.12710 Tosca EM, Gauderat G, Fouliard S, Burbridge M, Chenel M, Magni P (2021) Modeling restoration of gefitinib efficacy by co‐administration of MET inhibitors in an EGFR inhibitor‐resistant NSCLC xenograft model: A tumor‐in‐host DEB‐based approach. CPT pharmacometrics Syst Pharmacol 1–16. https://​doi.​org/​10.​1002/​psp4.​12710
17.
Zurück zum Zitat Poggesi I, De Nicolao G, Germani M, Rocchetti M (2009) Re: Antitumor efficacy testing in rodents. J Natl Cancer Inst 101(22):1592–1593CrossRef Poggesi I, De Nicolao G, Germani M, Rocchetti M (2009) Re: Antitumor efficacy testing in rodents. J Natl Cancer Inst 101(22):1592–1593CrossRef
18.
Zurück zum Zitat Rocchetti M, Poggesi I, Germani M, Fiorentini F, Pellizzoni C, Zugnoni P et al (2005) A pharmacokinetic-pharmacodynamic model for predicting tumour growth inhibition in mice: a useful tool in oncology drug development. Basic Clin Pharmacol Toxicol 96(3):265–268CrossRef Rocchetti M, Poggesi I, Germani M, Fiorentini F, Pellizzoni C, Zugnoni P et al (2005) A pharmacokinetic-pharmacodynamic model for predicting tumour growth inhibition in mice: a useful tool in oncology drug development. Basic Clin Pharmacol Toxicol 96(3):265–268CrossRef
19.
Zurück zum Zitat Rocchetti M, Simeoni M, Pesenti E, De Nicolao G, Poggesi I (2007) Predicting the active doses in humans from animal studies: a novel approach in oncology. Eur J Cancer 43:1862–1868CrossRef Rocchetti M, Simeoni M, Pesenti E, De Nicolao G, Poggesi I (2007) Predicting the active doses in humans from animal studies: a novel approach in oncology. Eur J Cancer 43:1862–1868CrossRef
20.
Zurück zum Zitat Bueno L, de Alwis DP, Pitou C, Yingling J, Lahn M, Glatt S et al (2008) 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):142–150CrossRef Bueno L, de Alwis DP, Pitou C, Yingling J, Lahn M, Glatt S et al (2008) 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):142–150CrossRef
21.
Zurück zum Zitat Salphati L, Wong H, Belvin M, Bradford D, Edgar KA, Prior WW et al (2010) Pharmacokinetic-pharmacodynamic modeling of tumor growth inhibition and biomarker modulation by the novel phosphatidylinositol 3-kinase inhibitor GDC-0941. Drug Metab Dispos 38(9):1436–1442CrossRef Salphati L, Wong H, Belvin M, Bradford D, Edgar KA, Prior WW et al (2010) Pharmacokinetic-pharmacodynamic modeling of tumor growth inhibition and biomarker modulation by the novel phosphatidylinositol 3-kinase inhibitor GDC-0941. Drug Metab Dispos 38(9):1436–1442CrossRef
22.
Zurück zum Zitat Ribba B, Watkin E, Tod M, Girard P, Grenier E, You B et al (2011) A model of vascular tumour growth in mice combining longitudinal tumour size data with histological biomarkers. Eur J Cancer 47(3):479–490CrossRef Ribba B, Watkin E, Tod M, Girard P, Grenier E, You B et al (2011) A model of vascular tumour growth in mice combining longitudinal tumour size data with histological biomarkers. Eur J Cancer 47(3):479–490CrossRef
24.
Zurück zum Zitat Lilian Y, Li YG, Gonzalez MOD (2019) Plasma protein binding of erdafitinib across clinical studies. In: Presented at American Society for Clinical Pharmacology and Therapeutics (ASCPT) 2019 Annual Meeting, Washington, DC Lilian Y, Li YG, Gonzalez MOD (2019) Plasma protein binding of erdafitinib across clinical studies. In: Presented at American Society for Clinical Pharmacology and Therapeutics (ASCPT) 2019 Annual Meeting, Washington, DC
25.
Zurück zum Zitat Dosne AG, Valade E, Stuyckens K, Li LY, Ouellet D, Perez-Ruixo JJ (2019) Population pharmacokinetics of total and free erdafitinib in adult healthy volunteers and cancer patients: analysis of phase 1 and phase 2 studies. J Clin Pharmacol 60(4):515–527CrossRef Dosne AG, Valade E, Stuyckens K, Li LY, Ouellet D, Perez-Ruixo JJ (2019) Population pharmacokinetics of total and free erdafitinib in adult healthy volunteers and cancer patients: analysis of phase 1 and phase 2 studies. J Clin Pharmacol 60(4):515–527CrossRef
26.
Zurück zum Zitat Loriot Y, Necchi A, Park SH, Garcia-Donas J, Huddart R, Burgess E et al (2019) Erdafitinib in locally advanced or metastatic urothelial carcinoma. N Engl J Med 38(1):338–348CrossRef Loriot Y, Necchi A, Park SH, Garcia-Donas J, Huddart R, Burgess E et al (2019) Erdafitinib in locally advanced or metastatic urothelial carcinoma. N Engl J Med 38(1):338–348CrossRef
27.
Zurück zum Zitat Venkatakrishnan K, Friberg LE, Ouellet D, Mettet Al JT, Stein A, Trocóniz IF et al (2015) Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities. Clin Pharmacol Ther 97(1):37–54CrossRef Venkatakrishnan K, Friberg LE, Ouellet D, Mettet Al JT, Stein A, Trocóniz IF et al (2015) Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities. Clin Pharmacol Ther 97(1):37–54CrossRef
28.
Zurück zum Zitat Perera TPS, Jovcheva E, Mevellec L, Vialard J, De Lange D, Verhulst T et al (2017) Discovery and pharmacological characterization of JNJ-42756493 (Erdafitinib), a functionally selective small-molecule FGFR family inhibitor. Mol Cancer Ther 16(6):1010–1020CrossRef Perera TPS, Jovcheva E, Mevellec L, Vialard J, De Lange D, Verhulst T et al (2017) Discovery and pharmacological characterization of JNJ-42756493 (Erdafitinib), a functionally selective small-molecule FGFR family inhibitor. Mol Cancer Ther 16(6):1010–1020CrossRef
29.
Zurück zum Zitat Workman P, Aboagye EO, Balkwill F, Balmain A, Bruder G, Chaplin DJ et al (2010) Guidelines for the welfare and use of animals in cancer research. Br J Cancer 102(11):1555–1577CrossRef Workman P, Aboagye EO, Balkwill F, Balmain A, Bruder G, Chaplin DJ et al (2010) Guidelines for the welfare and use of animals in cancer research. Br J Cancer 102(11):1555–1577CrossRef
30.
Zurück zum Zitat Tabernero J, Bahleda R, Dienstmann R, Infante JR, Mita A, Italiano A et al (2015) Phase I dose-escalation study of JNJ-42756493, an oral pan-fibroblast growth factor receptor inhibitor, in patients with advanced solid tumors. J Clin Oncol 33(30):3401–3408CrossRef Tabernero J, Bahleda R, Dienstmann R, Infante JR, Mita A, Italiano A et al (2015) Phase I dose-escalation study of JNJ-42756493, an oral pan-fibroblast growth factor receptor inhibitor, in patients with advanced solid tumors. J Clin Oncol 33(30):3401–3408CrossRef
31.
Zurück zum Zitat Bahleda R, Italiano A, Hierro C, Mita A, Cervantes A, Chan N et al (2019) Multicenter phase I study of erdafitinib (JNJ-42756493), oral pan-fibroblast growth factor receptor inhibitor, in patients with advanced or refractory solid tumors. Clin Cancer Res 25(16):4888–4897CrossRef Bahleda R, Italiano A, Hierro C, Mita A, Cervantes A, Chan N et al (2019) Multicenter phase I study of erdafitinib (JNJ-42756493), oral pan-fibroblast growth factor receptor inhibitor, in patients with advanced or refractory solid tumors. Clin Cancer Res 25(16):4888–4897CrossRef
32.
Zurück zum Zitat Cosson VF, Fuseau E, Efthymiopoulos C, Bye A (1997) Mixed effect modeling of sumatriptan pharmacokinetics during drug development. I: interspecies allometric scaling. J Pharmacokinet Biopharm 25(2):149–167CrossRef Cosson VF, Fuseau E, Efthymiopoulos C, Bye A (1997) Mixed effect modeling of sumatriptan pharmacokinetics during drug development. I: interspecies allometric scaling. J Pharmacokinet Biopharm 25(2):149–167CrossRef
33.
Zurück zum Zitat Boxenbaum H (1982) Interspecies scaling, allometry, physiological time, and the ground plan of pharmacokinetics. J Pharmacokinet Biopharm 10(2):201–227CrossRef Boxenbaum H (1982) Interspecies scaling, allometry, physiological time, and the ground plan of pharmacokinetics. J Pharmacokinet Biopharm 10(2):201–227CrossRef
34.
Zurück zum Zitat Tang H, Mayersohn M (2011) Controversies in allometric scaling for predicting human drug clearance: an historical problem and reflections on what works and what does not. Curr Top Med Chem 11:340–350CrossRef Tang H, Mayersohn M (2011) Controversies in allometric scaling for predicting human drug clearance: an historical problem and reflections on what works and what does not. Curr Top Med Chem 11:340–350CrossRef
35.
Zurück zum Zitat Mordenti J, Chen SA, Moore JA, Ferraiolo BL, Green JD (1991) Interspecies scaling of clearance and volume of distribution data for five therapeutic proteins. Pharm Res An Off J Am Assoc Pharm Sci 8(11):1351–1359 Mordenti J, Chen SA, Moore JA, Ferraiolo BL, Green JD (1991) Interspecies scaling of clearance and volume of distribution data for five therapeutic proteins. Pharm Res An Off J Am Assoc Pharm Sci 8(11):1351–1359
36.
Zurück zum Zitat Magni P, Simeoni M, Poggesi I, Rocchetti M, De Nicolao G (2006) A mathematical model to study the effects of drugs administration on tumor growth dynamics. Math Biosci 200(2):127–151CrossRef Magni P, Simeoni M, Poggesi I, Rocchetti M, De Nicolao G (2006) A mathematical model to study the effects of drugs administration on tumor growth dynamics. Math Biosci 200(2):127–151CrossRef
38.
Zurück zum Zitat Lestini G, Mentré F, Magni P (2016) Optimal design for informative protocols in xenograft tumor growth inhibition experiments in mice. AAPS J 18(5):1233–1243CrossRef Lestini G, Mentré F, Magni P (2016) Optimal design for informative protocols in xenograft tumor growth inhibition experiments in mice. AAPS J 18(5):1233–1243CrossRef
Metadaten
Titel
A translational model-based approach to inform the choice of the dose in phase 1 oncology trials: the case study of erdafitinib
verfasst von
E. M. Tosca
N. Terranova
K. Stuyckens
A. G. Dosne
T. Perera
J. Vialard
P. King
T. Verhulst
J. J. Perez-Ruixo
P. Magni
I. Poggesi
Publikationsdatum
17.11.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Cancer Chemotherapy and Pharmacology / Ausgabe 1/2022
Print ISSN: 0344-5704
Elektronische ISSN: 1432-0843
DOI
https://doi.org/10.1007/s00280-021-04370-7

Weitere Artikel der Ausgabe 1/2022

Cancer Chemotherapy and Pharmacology 1/2022 Zur Ausgabe

15% bedauern gewählte Blasenkrebs-Therapie

29.05.2024 Urothelkarzinom Nachrichten

Ob Patienten und Patientinnen mit neu diagnostiziertem Blasenkrebs ein Jahr später Bedauern über die Therapieentscheidung empfinden, wird einer Studie aus England zufolge von der Radikalität und dem Erfolg des Eingriffs beeinflusst.

Erhöhtes Risiko fürs Herz unter Checkpointhemmer-Therapie

28.05.2024 Nebenwirkungen der Krebstherapie Nachrichten

Kardiotoxische Nebenwirkungen einer Therapie mit Immuncheckpointhemmern mögen selten sein – wenn sie aber auftreten, wird es für Patienten oft lebensgefährlich. Voruntersuchung und Monitoring sind daher obligat.

Costims – das nächste heiße Ding in der Krebstherapie?

28.05.2024 Onkologische Immuntherapie Nachrichten

„Kalte“ Tumoren werden heiß – CD28-kostimulatorische Antikörper sollen dies ermöglichen. Am besten könnten diese in Kombination mit BiTEs und Checkpointhemmern wirken. Erste klinische Studien laufen bereits.

Perioperative Checkpointhemmer-Therapie verbessert NSCLC-Prognose

28.05.2024 NSCLC Nachrichten

Eine perioperative Therapie mit Nivolumab reduziert das Risiko für Rezidive und Todesfälle bei operablem NSCLC im Vergleich zu einer alleinigen neoadjuvanten Chemotherapie um über 40%. Darauf deuten die Resultate der Phase-3-Studie CheckMate 77T.

Update Onkologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.