Integrating antibody–drug conjugate bioanalytical measures using PK–PD modeling & simulation Review
quality-by-design into these molecules with the help of model-based drug development approach. Use of in vitro and preclinical data to make informed go/ no-go decision about moving an ADC to the clinic is expected to increase. Additionally, along with maxi- mum tolerated dose, pharmacologically active dose and optimal biological dose based paradigms are expected to increasingly contribute toward the selection of optimal clinical dosing regimen for ADCs.
Executive summary
• While it is important to generate good quality bioanalytical data, it is equally important to quantitatively integrate and comprehend all the data in order to enable the decision-making process during antibody–drug conjugate (ADC) development.
• Mathematical modeling and simulation (M&S) serves as an ideal tool for quantitatively integrating the PK, PD and toxicodynamic (TD) data generated during different stages of ADC development and conducting virtual experiments to assess the behavior of ADCs under different scenarios.
• In order to fully utilize the capability of PK–PD–TD M&S to enable successful ADC development, preclinical and clinical project teams involved in ADC development need to be multidisciplinary with both bioanalytical scientists and PK–PD M&S scientists working hand in hand.
• When the bioanalytical measures are committed with PK–PD M&S scientists’ perspective in mind it provides an opportunity to obtain better insight into the behavior of ADC from each experiment.
• This manuscript presents different case studies that highlight the application of PK/PD/TD M&S for integrating a diverse array of in vitro, preclinical and clinical data generated at different stages of ADC development.
• PK–PD M&S can provide an enhanced understanding about the PK, PD and TD behavior of ADCs, and a priori predict preclinical and clinical exposure and efficacy of ADCs.
References
1 Gorovits B, Alley SC, Bilic S et al. Bioanalysis of antibody– drug conjugates: American Association of Pharmaceutical Scientists Antibody–drug Conjugate Working Group position paper. Bioanalysis 5(9), 997–1006 (2013).
2 Han TH, Zhao B. Absorption, distribution, metabolism, and excretion considerations for the development of antibody–drug conjugates. Drug Metab. Dispos. 42(11), 1914–1920 (2014).
3
Singh AP, Shin YG, Shah DK. Application of pharmacokinetic-pharmacodynamic modeling and simulation for antibody–drug conjugate development. Pharm. Res. (2015) (Epub ahead of print).
4 5
Shen BQ, Xu K, Liu L et al. Conjugation site modulates the in vivo stability and therapeutic activity of antibody–drug conjugates. Nat. Biotechnol. 30(2), 184–189 (2012).
Jackson D, Atkinson J, Guevara CI et al. In vitro and in vivo evaluation of cysteine and site specific conjugated herceptin antibody–drug conjugates. PLoS ONE 9(1), e83865 (2014).
6 Bender B, Leipold DD, Xu K, Shen BQ, Tibbitts J, Friberg LE. A mechanistic pharmacokinetic model elucidating the disposition of trastuzumab emtansine (T-DM1), an antibody– drug conjugate (ADC) for treatment of metastatic breast cancer. AAPS J. 16(5), 994–1008 (2014).
7 Okeley NM, Miyamoto JB, Zhang X et al. Intracellular activation of SGN-35, a potent anti-CD30 antibody–drug conjugate. Clin. Cancer Res. 16(3), 888–897 (2010).
8
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
9
brentuximab-vedotin. J. Pharmacokinet. Pharmacodyn. 39(6), 643–659 (2012).
Shah DK, King LE, Han X et al. A priori prediction of tumor payload concentrations: preclinical case study with an auristatin-based anti-5T4 antibody–drug conjugate. AAPS J. 16(3), 452–463 (2014).
10 Lu D, Joshi A, Wang B et al. An integrated multiple-analyte pharmacokinetic model to characterize trastuzumab emtansine (T-DM1) clearance pathways and to evaluate reduced pharmacokinetic sampling in patients with HER2- positive metastatic breast cancer. Clin. Pharmacokinet. 52(8), 657–672 (2013).
11 Gibiansky L, Gibiansky E. Target-mediated drug disposition model and its approximations for antibody–drug conjugates. J. Pharmacokinet. Pharmacodyn. 41(1), 35–47 (2014).
12 Lu D, Jin JY, Girish S et al. Semi-mechanistic multiple- analyte pharmacokinetic model for an antibody–drug- conjugate in cynomolgus monkeys. Pharm. Res. 32(6), 1907–1919 (2014).
13 Chudasama VL, Schaedeli Stark F, Harrold JM et al. Semi-mechanistic population pharmacokinetic model of multivalent trastuzumab emtansine in patients with metastatic breast cancer. Clin. Pharmacol. Ther. 92(4), 520–527 (2012).
14 Sukumaran S, Gadkar K, Zhang C et al. Mechanism-based pharmacokinetic/pharmacodynamic model for THIOMAB drug conjugates. Pharm. Res. 32(6), 1884–1893 (2014).
15 Alley SC, Zhang X, Okeley NM et al. The pharmacologic basis for antibody-auristatin conjugate activity. J. Pharmacol. Exp. Ther. 330(3), 932–938 (2009).
Financial & competing interests disclosure The authors have no relevant affiliations or financial involve- ment with any organization or entity with a financial inter- est in or financial conflict with the subject matter or mate- rials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this
manuscript.
future science group
www.future-science.com
1647
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148 |
Page 149 |
Page 150 |
Page 151 |
Page 152 |
Page 153 |
Page 154