Clinical pharmacology to support monoclonal antibody drug development

Sharon Lu; Sharon Lu

doi:10.3934/medsci.2022014

AIMS Medical Science

2022, Volume 9, Issue 2: 322-341. doi: 10.3934/medsci.2022014

Previous Article Next Article

Review

Clinical pharmacology to support monoclonal antibody drug development

Sharon Lu ^,

Scholar Rock, 301 Binney Street 3^rd floor, Cambridge, Massachusetts 02142, affiliation at the time of the study, USA

Received: 22 December 2021 Revised: 29 March 2022 Accepted: 19 April 2022 Published: 10 May 2022

Citation: Sharon Lu. Clinical pharmacology to support monoclonal antibody drug development[J]. AIMS Medical Science, 2022, 9(2): 322-341. doi: 10.3934/medsci.2022014

Related Papers:

Abbreviations

QSP

Quantitative systems pharmacology

BLA

Biologics license application

Pharmacokinetics

Pharmacodynamics

DDI

Drug-drug interaction

FIH

First in human

NOAEL

No observed adverse effect level

MABEL

Minimum anticipated biological effect level

HED

Human equivalent dose

STD10

Dose severely toxic to 10% rodent

MTD

Maximum tolerated dose

Pharmacology activity

Receptor occupancy

HNSTD

Highest non-severely toxic dose

TMDD

Target mediated drug disposition

MID3

Model-informed drug discovery and development

iMID3

Integrated model-informed drug discovery and development

TQT study

Thorough QT/QTc study

Acknowledgments

Author would like to thank George Nomikos, MD PhD, Amy Place, PhD, Hongcheng Liu, PhD, Deborah Meshulam, PhD, Jose Rossello, MD, and Nathalie Kertesz, PhD at Scholar Rock for scientific input and technical review.

Conflict of interest

The author declares no conflict of interest.

References

[1]	Mullard A (2021) FDA approves 100th monoclonal antibody product. Nat Rev Drug Discov 20: 491-495. https://doi.org/10.1038/d41573-021-00079-7
[2]	Frei JC, Lai JR (2016) Protein and antibody engineering by phage display. Methods Enzymol 580: 45-87. https://doi.org/10.1016/bs.mie.2016.05.005
[3]	Zhao J, Nussinov R, Wu WJ, et al. (2018) In silico methods in antibody design. Antibodies (Basel) 7: 22. https://doi.org/10.3390/antib7030022
[4]	Booth BJ, Ramakrishnan B, Narayan K, et al. (2018) Extending human IgG half-life using structure-guided design. MAbs 10: 1098-1110. https://doi.org/10.1080/19420862.2018.1490119
[5]	Zou P, Yu Y, Zheng N, et al. (2012) Applications of human pharmacokinetic prediction in first-in-human dose estimation. AAPS J 14: 262-281. https://doi.org/10.1208/s12248-012-9332-y
[6]	Tam K (2013) Estimating the “first in human” dose—a revisit with particular emphasis on oncology drugs. ADMET DMPK 1: 63-75. https://doi.org/10.5599/admet.1.4.10
[7]	U.S. Food & Drug Administration, Guidance for industry: Estimating the maximum safe starting dose in initial clinical trials for therapeutics in adult healthy volunteers, 2005. Available from: https://www.fda.gov/media/72309/download
[8]	European Medicines Agency, Guideline on strategies to identify and mitigate risks for first-in-human clinical trials with investigational medicinal products, 2007. Available from: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-strategies-identify-mitigate-risks-first-human-clinical-trials-investigational-medicinal_en.pdf
[9]	Senderowicz AM (2010) Information needed to conduct first-in-human oncology trials in the United States: a view from a former FDA medical reviewer. Clin Cancer Res 16: 1719-1725. https://doi.org/10.1158/1078-0432.CCR-09-2766
[10]	Olson H, Betton G, Robinson D, et al. (2000) Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul Toxicol Pharmacol 32: 56-67. https://doi.org/10.1006/rtph.2000.1399
[11]	Tibbitts J, Cavagnaro JA, Haller CA, et al. (2010) Practical approaches to dose selection for first-in-human clinical trials with novel biopharmaceuticals. Regul Toxicol Pharmacol 58: 243-251. https://doi.org/10.1016/j.yrtph.2010.06.007
[12]	Saber H, Gudi R, Manning M, et al. (2016) An FDA oncology analysis of immune activating products and first-in-human dose selection. Regul Toxicol Pharmacol 81: 448-456. https://doi.org/10.1016/j.yrtph.2016.10.002
[13]	Mittapalli RK, Liao K, Williams J, et al. (2021) Optimal dose selection in oncology biologics development: Need for a paradigm shift. J Clin Oncol 39: 3093. https://doi.org/10.1200/JCO.2021.39.15_suppl.3093
[14]	Paccaly AJ, Migden MR, Papadopoulos KP, et al. (2021) Fixed dose of cemiplimab in patients with advanced malignancies based on population pharmacokinetic analysis. Adv Ther 38: 2365-2378. https://doi.org/10.1007/s12325-021-01638-5
[15]	Brahmer JR, Drake CG, Wollner I, et al. (2010) Phase I study of single-agent anti-programmed death-1 (MDX-1106) in refractory solid tumors: safety, clinical activity, pharmacodynamics, and immunologic correlates. J Clin Oncol 28: 3167-3175. https://doi.org/10.1200/JCO.2009.26.7609
[16]	Patnaik A, Kang SP, Rasco D, et al. (2015) Phase I study of pembrolizumab (MK-3475; anti-PD-1 monoclonal antibody) in patients with advanced solid tumors. Clin Cancer Res 21: 4286-4293. https://doi.org/10.1200/JCO.2009.26.7609
[17]	Pink Sheet, US FDA's “Project Optimus” will encourage move away from conventional dose-finding for modern cancer therapies by Rawson K, 2021. Available from: https://pink.pharmaintelligence.informa.com/PS144387/US-FDAs-Project-Optimus-Will-Encourage-Move-Away-From-Conventional-Dose-Finding-For-Modern-Cancer-Therapies
[18]	U.S. Food & Drug Administration, Guidance for Industry: Immunogenicity testing of therapeutic protein products—developing and validating assays for anti-drug antibody detection, 2019. Available from: https://www.fda.gov/media/119788/download
[19]	U.S. Food & Drug Administration, Guidance for Industry: Immunogenicity assessment for therapeutic protein products, 2014. Available from: https://www.fda.gov/media/85017/download
[20]	European Medicines Agency, Guideline on Immunogenicity assessment of therapeutic proteins, 2017. Available from: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-immunogenicity-assessment-therapeutic-proteins-revision-1_en.pdf
[21]	Lu S, Ronald RB, Clancy A, et al. (2021) An integrated analysis of dostarlimab immunogenicity. AAPS J 23: 96. https://doi.org/10.1208/s12248-021-00624-7
[22]	Bettsa A, Keunecke A, Steegb TJ, et al. (2018) Linear pharmacokinetic parameters for monoclonal antibodies are similar within a species and across different pharmacological targets: A comparison between human, cynomolgus monkey and hFcRn Tg32 transgenic mouse using a population-modeling approach. MAbs 10: 751-764. https://doi.org/10.1080/19420862.2018.1462429
[23]	Bai S, Jorga K, Xin Y, et al. (2012) A guide to rational dosing of monoclonal antibodies. Clin Pharmacokinet 51: 119-135. https://doi.org/10.2165/11596370-000000000-00000
[24]	Hendrikx J, Haanen J, Voest EE, et al. (2017) Fixed dosing of monoclonal antibodies in oncology. Oncologist 22: 1212-1221. https://doi.org/10.1634/theoncologist.2017-0167
[25]	Ryman JT, Meibohm B (2017) Pharmacokinetics of monoclonal antibodies. CPT Pharmacometrics Syst Pharmacol 6: 576-588. https://doi.org/10.1002/psp4.12224
[26]	Section 12.3 Pharmacokinetics of prescribing information: KEYTRUDA (pembrolizumab) injection. Available from: https://www.accessdata.fda.gov/drugsatfda_docs/label/2022/125514s110lbl.pdf
[27]	Section 12.3 Pharmacokinetics of prescribing information: OPDIVO (nivolumab) injection. Available from: https://www.accessdata.fda.gov/drugsatfda_docs/label/2022/125554s112lbl.pdf
[28]	Section 12.3 Pharmacokinetics of prescribing information: LIBTAYO (cemiplimab-rwlc) injection. Available from: https://www.accessdata.fda.gov/drugsatfda_docs/label/2021/761097s007lbl.pdf
[29]	Melhem M, Hanze E, Lu S, et al. (2022) Population pharmacokinetics and exposure-response of anti-programmed cell death protein-1 monoclonal antibody dostarlimab in advanced solid tumors. Br J Clin Pharmacol . https://doi.org/10.1111/bcp.15339
[30]	Section 12.3 Pharmacokinetics of prescribing information: TECENTRIQ (atezolizumab) injection. Available from: https://www.accessdata.fda.gov/drugsatfda_docs/label/2022/761034s043lbl.pdf
[31]	Section 12.3 Pharmacokinetics of prescribing information: BAVENCIO (avelumab) injection. Available from: https://www.accessdata.fda.gov/drugsatfda_docs/label/2020/761049s005lbl.pdf
[32]	Section 12.3 Pharmacokinetics of prescribing information: IMFINZI (durvalumab) injection. Available from: https://www.accessdata.fda.gov/drugsatfda_docs/label/2021/761069s028lbl.pdf
[33]	Sheng J, Srivastava S, Sanghavi K, et al. (2017) Clinical pharmacology considerations for the development of immune checkpoint inhibitors. J Clin Pharmacol 57: S26-S42. https://doi.org/10.1002/jcph.990
[34]	Liu L (2018) Pharmacokinetics of monoclonal antibodies and Fc-fusion proteins. Protein Cell 9: 15-32. https://doi.org/10.1007/s13238-017-0408-4
[35]	Melhem M, Hanze E, Lu S, et al. (2021) Population pharmacokinetics and exposure-response analysis of the anti-programmed cell death-1 receptor monoclonal antibody dostarlimab in recurrent/advanced solid tumors with mismatch repair-deficiency. ACoP12 .
[36]	Liu C, Yu J, Li H, et al. (2017) Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis. Clin Pharmacol Ther 101: 657-666. https://doi.org/10.1002/cpt.656
[37]	Turner DC, Kondic AG, Anderson KM, et al. (2018) Pembrolizumab exposure-response assessments challenged by association of cancer cachexia and catabolic clearance. Clin Cancer Res 24: 5841-5849. https://doi.org/10.1158/1078-0432.CCR-18-0415
[38]	U.S. Food & Drug Administration, Drug-drug interaction assessment for therapeutic proteins—Guidance for industry, 2020. Available from: https://www.fda.gov/media/140909/download
[39]	Lee J, Zhang L, Men AY, et al. (2010) CYP-mediated therapeutic protein-drug interactions: clinical findings, proposed mechanisms and regulatory implications. Clin Pharmacokinet 49: 295-310. https://doi.org/10.2165/11319980-000000000-00000
[40]	Coutant DE, Hall SD (2018) Disease-drug interactions in inflammatory states via effects on CYP-mediated drug clearance. J Clin Pharmacol 58: 849-863. https://doi.org/10.1002/jcph.1093
[41]	Abuqayyas L, Balthasar JP (2012) Pharmacokinetic mAb-mAb interaction: Anti-VEGF mAb decreases the distribution of anti-CEA mAb into colorectal tumor xenografts. AAPS J 14: 445-455. https://doi.org/10.1208/s12248-012-9357-2
[42]	Pastuskovas CV, Mundo EE, Williams SP, et al. (2012) Effects of anti-VEGF on pharmacokinetics, biodistribution, and tumor penetration of trastuzumab in a preclinical breast cancer model. Mol Cancer Ther 11: 752-762. https://doi.org/10.1158/1535-7163.MCT-11-0742-T
[43]	Kiessling P, Lledo-Garcia R, Watanabe S, et al. (2017) The FcRn inhibitor rozanolixizumab reduces human serum IgG concentration: A randomized phase 1 study. Sci Transl Med 9: 1208. https://doi.org/10.1126/scitranslmed.aan1208
[44]	Paglialunga S, Offman E, Ichhpurani N, et al. (2017) Update and trends on pharmacokinetic studies in patients with impaired renal function: practical insight into application of the FDA and EMA guidelines. Expert Rev Clin Pharmacol 10: 273-283. https://doi.org/10.1080/17512433.2017.1274651
[45]	U.S. Food & Drug Administration, Guidance for Industry: Pharmacokinetics in patients with impaired renal function—study design, data analysis, and impact on dosing, 2020. Available from: https://www.fda.gov/media/78573/download
[46]	European Medicines Agency, Guideline on the clinical investigation of the pharmacokinetics of therapeutic proteins, 2007. Available from: https://www.ema.europa.eu/en/clinical-investigation-pharmacokinetics-therapeutic-proteins
[47]	U.S. Food & Drug Administration, Guidance for Industry: Pharmacokinetics in patients with impaired hepatic function: Study design, data analysis, and impact on dosing and labeling. Available from: https://www.fda.gov/media/71311/download
[48]	Ryman JT, Meibohm B (2017) Pharmacokinetics of monoclonal antibodies. CPT Pharmacometrics Syst Pharmacol 6: 576-588. https://doi.org/10.1002/psp4.12224
[49]	Sun Q, Seo S, Zvada S, et al. (2020) Does hepatic impairment affect the exposure of monoclonal antibodies?. Clin Pharmacol Ther 107: 1256-1262. https://doi.org/10.1002/cpt.1765
[50]	Yao LP (2018) Extrapolation in pediatric drug development: An evolving science. FDA Biopharm Workshop.
[51]	Karesh A FDA Pediatric drug development: Regulatory expectations (BASIC). Available from: https://www.fda.gov/files/drugs/published/Pediatric-Drug-Development--Regulatory-Expectations---BASIC.pdf
[52]	U.S. Food & Drug Administration, Pediatric study plans: Content of and process for submitting initial pediatric study plans and amended initial pediatric study plans—Guidance for industry, 2020. Available from: https://www.fda.gov/media/86340/download
[53]	U.S. Food & Drug Administration, FDARA Implementation guidance for pediatric studies of molecularly targeted oncology drugs: Amendments to Sec. 505B of the FD &C Act—Guidance for Industry, 2021. Available from: https://www.fda.gov/media/133440/download
[54]	Ollivier C, Mulugeta YL, Ruggieri L, et al. (2019) Paediatric extrapolation: A necessary paradigm shift. Br J Clin Pharmacol 85: 675-679. https://doi.org/10.1111/bcp.13809
[55]	Sun H, Temeck JW, Chambers W, et al. (2017) Extrapolation of efficacy in pediatric drug development and evidence-based medicine: Progress and lessons learned. Ther Innov Regul Sci 2017: 1-7.
[56]	International Council on Harmonisation of TechnicalICH Harmonised Guideling: Addendum to ICH E11: Clinical investigation of medicinal prodicinal products in the pediatric population population E11 (R1) (2017). Available from: https://www.cfdi.org.cn/resource/attachment/ueFile/2018/01/TW9uIEphbiAwOCAwOTo1OToxOSAyMDE4MjQ3MDQ=.pdf
[57]	European Medicines Agency, Reflection paper on the use of extrapolation in the development of medicines for paediatrics, 2018. Available from: https://www.ema.europa.eu/en/documents/scientific-guideline/adopted-reflection-paper-use-extrapolation-development-medicines-paediatrics-revision-1_en.pdf
[58]	Robbie GJ, Zhao L, Mondick J, et al. (2012) Population pharmacokinetics of palivizumab, a humanized anti-respiratory syncytial virus monoclonal antibody, in adults and children. Antimicrob Agents Chemother 56: 4927-4936. https://doi.org/10.1128/AAC.06446-11
[59]	Hardiansyah D, Ng CM (2018) Effects of the FcRn developmental pharmacology on the pharmacokinetics of therapeutic monoclonal IgG antibody in pediatric subjects using minimal physiologically-based pharmacokinetic modelling. MAbs 10: 1144-1156. https://doi.org/10.1080/19420862.2018.1494479
[60]	Pan X, Stader F, Abduljalil K, et al. (2020) Development and application of a physiologically-based pharmacokinetic model to predict the pharmacokinetics of therapeutic proteins from full-term neonates to adolescents. AAPS J 22: 76. https://doi.org/10.1208/s12248-020-00460-1
[61]	U.S. Food & Drug Administration, FDA draft guidance for industry: Bioavailability and bioequivalence studies submitted in NDAs or INDs—General Considerations, 2014. Available from: https://www.fda.gov/media/88254/download
[62]	U.S. Food & Drug Administration, Guidance for industry: Statistics approaches to establishing bioequivalence, 2001. Available from: https://www.fda.gov/media/70958/download
[63]	Li ZY, Easton R (2018) Practical considerations in clinical strategy to support the development of injectable drug-device combination products for biologics. MAbs 10: 18-33. https://doi.org/10.1080/19420862.2017.1392424
[64]	European Medicines Agency, Guideline on comparability of biotechnology-derived medicinal products after a change in the manufacturing process: Non-clinical and clinical issues, 2007. Available from: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-comparability-biotechnology-derived-medicinal-products-after-change-manufacturing-process_en.pdf
[65]	U.S. Food & Drug Administration, Human factors studies and related clinical study considerations in combination product design and development—Draft guidance for industry and FDA staff, 2016. Available from: https://www.fda.gov/media/96018/download
[66]	Pecoraro V, De Santis E, Melegari A, et al. (2017) The impact of immunogenicity of TNFα inhibitors in autoimmune inflammatory disease. A systematic review and meta-analysis. Autoimmun Rev 16: 564-575. https://doi.org/10.1016/j.autrev.2017.04.002
[67]	Bahrami AA, Payandeh Z, Khalili S, et al. (2019) Immunoinformatics: in silico approaches and computational design of a multi-epitope, immunogenic protein. Int Rev Immunol 38: 307-322. https://doi.org/10.1080/08830185.2019.1657426
[68]	Seyhan AA (2019) Lost in translation: the valley of death across preclinical and clinical divide-identification of problems and overcoming obstacles. Transl Med Commun 4: 18. https://doi.org/10.1186/s41231-019-0050-7
[69]	Fleming N (2018) How artificial intelligence is changing drug discovery. Nature 557: S55-S57. https://doi.org/10.1038/d41586-018-05267-x
[70]	Marshall S, Madabushi R, Manolis E, et al. (2019) Model-informed drug discovery and development: Current industry good practice and regulatory expectations and future perspectives. CPT Pharmacometrics Syst Pharmacol 8: 87-96. https://doi.org/10.1002/psp4.12372
[71]	Chelliah V, Lazarou G, Bhatnagar S, et al. (2021) Quantitative systems pharmacology approaches for immuno-oncology: Adding virtual patients to the development paradigm. Clin Pharmacol Ther 109: 605-618. https://doi.org/10.1002/cpt.1987
[72]	Ma H, Wang H, Sové RJ, et al. (2020) A quantitative systems pharmacology model of T cell engager applied to solid tumor. AAPS J 22: 85. https://doi.org/10.1208/s12248-020-00450-3
[73]	Wang H, Ma H, Sové RJ, et al. (2021) Quantitative systems pharmacology model predictions for efficacy of atezolizumab and nab-paclitaxel in triple-negative breast cancer. J Immunother Cancer 9: e002100. https://doi.org/10.1136/jitc-2020-002100
[74]	Gong C, Milberg O, Wang B, et al. (2017) A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition. J R Soc Interface 14: 20170320. https://doi.org/10.1098/rsif.2017.0320
[75]	Lou H, Hageman MJ (2021) Machine learning attempts for predicting human subcutaneous bioavailability of monoclonal antibodies. Pharm Res 38: 451-460. https://doi.org/10.1007/s11095-021-03022-y
[76]	Terranova N, Venkatakrishnan K, Benincosa LJ (2021) Application of machine learning in translational medicine: Current status and future opportunities. AAPS J 23: 74. https://doi.org/10.1208/s12248-021-00593-x
[77]	Mawdsley D, Bennetts M, Dias S, et al. (2016) Model-based network meta-analysis: A framework for evidence synthesis of clinical trial data. CPT Pharmacometrics Syst Pharmacol 5: 393-401. https://doi.org/10.1002/psp4.12091
[78]	Mandema JM, Cox E, Alderman J (2005) Therapeutic benefit of eletriptan compared to sumatriptan for the acute relief of migraine pain—results of a model-based meta-analysis that accounts for encapsulation. Cephalalgia 25: 715-725. https://doi.org/10.1111/j.1468-2982.2004.00939.x
[79]	Zineh I (2019) Quantitative systems pharmacology: A regulatory perspective on translation. CPT Pharmacometrics Syst Pharmacol 8: 336-339. https://doi.org/10.1002/psp4.12403
[80]	Liu XY, Liu C, Huang RH, et al. (2021) Long short-term memory recurrent neural network for pharmacokinetic-pharmacodynamic modeling. Int J Clin Pharmacol Ther 59: 138-146. https://doi.org/10.5414/CP203800
[81]	Hart T, Xie L (2016) Providing data science support for systems pharmacology and its implications to drug discovery. Expert Opin Drug Discov 11: 241-256. https://doi.org/10.1517/17460441.2016.1135126

Reader Comments

Your name:*

Email:*
© 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)