Background Antibody Drug Conjugate (ADCs) therapies combine targeted antibodies with potent drugs. However, systemic toxicities often limit their effectiveness and narrow their therapeutic index (TI).1 While there are methods to predict the initial dose for ADCs based on efficacy,2 predicting toxicities that determine the maximum tolerated dose (MTD) remains a challenge. However, Inability to identify the MTD can lead to drug discontinuation.3 4
ADC programs face major challenges in selecting the right components, inferring human dose from preclinical data, and improving the TI. To address these challenges, we developed a mechanistic model. Here, we present two case studies: 1) How to select the appropriate payload based on cancer type? and 2) Deriving the dose for First-In-Human (FIH) trials while considering efficacy and toxicity. We also propose a framework for predicting MTD based on information from approved ADCs.
Methods We built an mPBPK model with detailed mechanisms for ADC internalization (figure 1). In case study 1, we compared Dxd and DM1 payloads5–7 based on their cell killing potential, bystander effect, and dose sensitivity. Case study 2 involved predicting the FIH dose for Lonca and comparing it to clinical data.8 9 We relied on published data for both case studies. For MTD prediction, we hypothesized that the plasma Cmax of the payload at MTD (PCmaxPL) should be similar for ADCs sharing the same payload (figure 2). Here we present the observations from 6 ADCs sharing MMAE payload.
Results We observed that Dxd and DM1 had similar potency, but Dxd exhibited a stronger bystander effect, making it suitable for heterogeneous lesions. DM1 was more sensitive to dose, but at high doses, its response was comparable to Dxd (figure 3A). Our model accurately predicted the FIH dose for Lonca, aligning with clinical recommendations (figure 3B). Consistency in PCmaxPL values (3–8 ng/mL) was observed for selected ADCs sharing the MMAE payload,10–15 supporting our hypothesis.
Conclusions ADCs require customization for specific cancer types and populations. Payload selection should consider factors beyond potency, including bystander effect and systemic toxicity. Our mechanistic ADC model reliably predicts the FIH dose based on preclinical data and the consistency in clinical PCmaxPL aids in predicting MTD for novel ADCs, enhancing FIH dosing recommendations.
Liao MZ, Lu D, Kågedal M, Miles D, Samineni D, Liu SN, et al. Model-Informed Therapeutic Dose Optimization Strategies for Antibody-Drug Conjugates in Oncology: What Can We Learn From US Food and Drug Administration-Approved Antibody-Drug Conjugates? Clin Pharmacol Ther. 2021 Nov;110(5):1216–30.
Haddish-Berhane N, Shah DK, Ma D, Leal M, Gerber HP, Sapra P, et al. On translation of antibody drug conjugates efficacy from mouse experimental tumors to the clinic: a PK/PD approach. J Pharmacokinet Pharmacodyn. 2013 Oct;40(5):557–71.
Donaghy H. Effects of antibody, drug and linker on the preclinical and clinical toxicities of antibody-drug conjugates. mAbs. 2016 May 18;8(4):659–71.
Annunziata CM, Kohn EC, LoRusso P, Houston ND, Coleman RL, Buzoianu M, et al. Phase 1, open-label study of MEDI-547 in patients with relapsed or refractory solid tumors. Invest New Drugs. 2013 Feb;31(1):77–84.
Singh AP, Maass KF, Betts AM, Wittrup KD, Kulkarni C, King LE, et al. Evolution of Antibody-Drug Conjugate Tumor Disposition Model to Predict Preclinical Tumor Pharmacokinetics of Trastuzumab-Emtansine (T-DM1). AAPS J. 2016 Jul;18(4):861–75.
Okamoto H, Oitate M, Hagihara K, Shiozawa H, Furuta Y, Ogitani Y, et al. Pharmacokinetics of trastuzumab deruxtecan (T-DXd), a novel anti-HER2 antibody-drug conjugate, in HER2-positive tumour-bearing mice. Xenobiotica. 2020 Oct 2;50(10):1242–50.
Ogitani Y, Hagihara K, Oitate M, Naito H, Agatsuma T. Bystander killing effect of DS -8201a, a novel anti-human epidermal growth factor receptor 2 antibody-drug conjugate, in tumors with human epidermal growth factor receptor 2 heterogeneity. Cancer Sci. 2016 Jul;107(7):1039–46.
Zammarchi F, Corbett S, Adams L, Tyrer PC, Kiakos K, Janghra N, et al. ADCT-402, a PBD dimer-containing antibody drug conjugate targeting CD19-expressing malignancies. Blood. 2018 Mar 8;131(10):1094–105.
Kahl BS, Hamadani M, Radford J, Carlo-Stella C, Caimi P, Reid E, et al. A Phase I Study of ADCT-402 (Loncastuximab Tesirine), a Novel Pyrrolobenzodiazepine-Based Antibody-Drug Conjugate, in Relapsed/Refractory B-Cell Non-Hodgkin Lymphoma. Clin Cancer Res. 2019 Dec 1;25(23):6986–94.
Rosenberg JE, Heath E, Perez R, Merchan J, Lang J, Ruether D, et al. Interim analysis of a phase I dose escalation trial of ASG-22CE (ASG-22ME; enfortumab vedotin), an antibody drug conjugate (ADC), in patients (Pts) with metastatic urothelial cancer (mUC). Ann Oncol. 2016 Oct;27:vi273.
Demetri GD, Luke JJ, Hollebecque A, Powderly JD, Spira AI, Subbiah V, et al. First-in-Human Phase I Study of ABBV-085, an Antibody-Drug Conjugate Targeting LRRC15, in Sarcomas and Other Advanced Solid Tumors. Clin Cancer Res. 2021 Jul 1;27(13):3556–66.
Camidge DR, Morgensztern D, Heist RS, Barve M, Vokes E, Goldman JW, et al. Phase I Study of 2- or 3-Week Dosing of Telisotuzumab Vedotin, an Antibody-Drug Conjugate Targeting c-Met, Monotherapy in Patients with Advanced Non-Small Cell Lung Carcinoma. Clin Cancer Res. 2021 Nov 1;27(21):5781–92.
Chen Y, Samineni D, Mukadam S, Wong H, Shen BQ, Lu D, et al. Physiologically Based Pharmacokinetic Modeling as a Tool to Predict Drug Interactions for Antibody-Drug Conjugates. Clin Pharmacokinet. 2015 Jan;54(1):81–93.
Wang ML, Barrientos JC, Furman RR, Mei M, Barr PM, Choi MY, et al. Zilovertamab Vedotin Targeting of ROR1 as Therapy for Lymphoid Cancers. NEJM Evid. 2022 Jan 9 [cited 2023 Jun 27];1(1).
Gibiansky L, Passey C, Voellinger J, Gunawan R, Hanley WD, Gupta M, et al. Population pharmacokinetic analysis for tisotumab vedotin in patients with locally advanced and/or metastatic solid tumors. CPT Pharmacomet Syst Pharmacol. 2022 Oct;11(10):1358–70.
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