Gupta P, Makowski EK, Kumar S, Zhang Y, Scheer JM, Tessier PM. Antibodies with weakly basic isoelectric points minimize trade-offs between formulation and physiological colloidal properties. Mol Pharm. 2022;19(3):775–87. https://doi.org/10.1021/acs.molpharmaceut.1c00373.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Dostalek M, Prueksaritanont T, Kelley RF. Pharmacokinetic de-risking tools for selection of monoclonal antibody lead candidates. MAbs. 2017;9(5):756–66. https://doi.org/10.1080/19420862.2017.1323160.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kingsbury JS, Saini A, Auclair SM, Fu L, Lantz MM, Halloran KT, et al. A single molecular descriptor to predict solution behavior of therapeutic antibodies. Sci Adv. 2020;6(32):eabb0372. https://doi.org/10.1126/sciadv.abb0372.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jain T, Sun TW, Durand S, Hall A, Houston NR, Nett JH, et al. Biophysical properties of the clinical-stage antibody landscape. Proc Natl Acad Sci USA. 2017;114(5):944–9. https://doi.org/10.1073/pnas.1616408114.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Makowski EK, Wu L, Gupta P, Tessier PM. Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods. MAbs. 2021;13(1):1895540. https://doi.org/10.1080/19420862.2021.1895540.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Negron C, Fang J, McPherson MJ, Stine WB Jr, McCluskey AJ. Separating clinical antibodies from repertoire antibodies, a path to in silico developability assessment. MAbs. 2022;14(1):2080628. https://doi.org/10.1080/19420862.2022.2080628.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Raybould MIJ, Marks C, Krawczyk K, Taddese B, Nowak J, Lewis AP, et al. Five computational developability guidelines for therapeutic antibody profiling. Proc Natl Acad Sci USA. 2019;116(10):4025–30. https://doi.org/10.1073/pnas.1810576116.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ahmed L, Gupta P, Martin KP, Scheer JM, Nixon AE, Kumar S (2021) Intrinsic physicochemical profile of marketed antibody-based biotherapeutics. Proc Natl Acad Sci U S A. 118(37). https://doi.org/10.1073/pnas.2020577118.

Zhang Y, Wu L, Gupta P, Desai AA, Smith MD, Rabia LA, et al. Physicochemical rules for identifying monoclonal antibodies with drug-like specificity. Mol Pharm. 2020;17(7):2555–69. https://doi.org/10.1021/acs.molpharmaceut.0c00257.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Shehata L, Maurer DP, Wec AZ, Lilov A, Champney E, Sun T, et al. Affinity maturation enhances antibody specificity but compromises conformational stability. Cell Rep. 2019;28(13):3300-8-e4. https://doi.org/10.1016/j.celrep.2019.08.056.

Article  CAS  Google Scholar 

Starr CG, Tessier PM. Selecting and engineering monoclonal antibodies with drug-like specificity. Curr Opin Biotechnol. 2019;60:119–27. https://doi.org/10.1016/j.copbio.2019.01.008.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kelly RL, Sun T, Jain T, Caffry I, Yu Y, Cao Y, et al. High throughput cross-interaction measures for human IgG1 antibodies correlate with clearance rates in mice. MAbs. 2015;7(4):770–7. https://doi.org/10.1080/19420862.2015.1043503.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hotzel I, Theil FP, Bernstein LJ, Prabhu S, Deng R, Quintana L, et al. A strategy for risk mitigation of antibodies with fast clearance. MAbs. 2012;4(6):753–60. https://doi.org/10.4161/mabs.22189.

Article  PubMed  PubMed Central  Google Scholar 

Kraft TE, Richter WF, Emrich T, Knaupp A, Schuster M, Wolfert A, et al. Heparin chromatography as an in vitro predictor for antibody clearance rate through pinocytosis. Mabs. 2020;12(1):1683432. https://doi.org/10.1080/19420862.2019.1683432.

Article  CAS  PubMed  Google Scholar 

Avery LB, Wade J, Wang M, Tam A, King A, Piche-Nicholas N, et al. Establishing in vitro in vivo correlations to screen monoclonal antibodies for physicochemical properties related to favorable human pharmacokinetics. MAbs. 2018;10(2):244–55. https://doi.org/10.1080/19420862.2017.1417718.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sharma VK, Patapoff TW, Kabakoff B, Pai S, Hilario E, Zhang B, et al. In silico selection of therapeutic antibodies for development: viscosity, clearance, and chemical stability. Proc Natl Acad Sci U S A. 2014;111(52):18601–6. https://doi.org/10.1073/pnas.1421779112.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Grinshpun B, Thorsteinson N, Pereira JN, Rippmann F, Nannemann D, Sood VD, et al. Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies. MAbs. 2021;13(1):1932230. https://doi.org/10.1080/19420862.2021.1932230.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chung S, Nguyen V, Lin YL, Lafrance-Vanasse J, Scales SJ, Lin K, et al. An in vitro FcRn- dependent transcytosis assay as a screening tool for predictive assessment of nonspecific clearance of antibody therapeutics in humans. MAbs. 2019;11(5):942–55. https://doi.org/10.1080/19420862.2019.1605270.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Goulet DR, Watson MJ, Tam SH, Zwolak A, Chiu ML, Atkins WM, et al. Toward a combinatorial approach for the prediction of IgG half-life and clearance. Drug Metab Dispos. 2018;46(12):1900–7. https://doi.org/10.1124/dmd.118.081893.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Datta-Mannan A, Thangaraju A, Leung D, Tang Y, Witcher DR, Lu JR, et al. Balancing charge in the complementarity-determining regions of humanized mAbs without affecting pI reduces non-specific binding and improves the pharmacokinetics. MAbs. 2015;7(3):483–93. https://doi.org/10.1080/19420862.2015.1016696.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Schoch A, Kettenberger H, Mundigl O, Winter G, Engert J, Heinrich J, et al. Charge-mediated influence of the antibody variable domain on FcRn-dependent pharmacokinetics. Proc Natl Acad Sci U S A. 2015;112(19):5997–6002. https://doi.org/10.1073/pnas.1408766112.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Liu S, Verma A, Kettenberger H, Richter WF, Shah DK. Effect of variable domain charge on in vitro and in vivo disposition of monoclonal antibodies. MAbs. 2021;13(1):1993769. https://doi.org/10.1080/19420862.2021.1993769.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bumbaca Yadav D, Sharma VK, Boswell CA, Hotzel I, Tesar D, Shang Y, et al. Evaluating the use of antibody variable region (Fv) charge as a risk assessment tool for predicting typical cynomolgus monkey pharmacokinetics. J Biol Chem. 2015;290(50):29732–41. https://doi.org/10.1074/jbc.M115.692434.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Li B, Tesar D, Boswell CA, Cahaya HS, Wong A, Zhang J, et al. Framework selection can influence pharmacokinetics of a humanized therapeutic antibody through differences in molecule charge. MAbs. 2014;6(5):1255–64. https://doi.org/10.4161/mabs.29809.

Article  PubMed  PubMed Central  Google Scholar 

Stüber JC, Rechberger KF, Miladinović SM, Pöschinger T, Zimmermann T, Villenave R, et al. Impact of charge patches on tumor disposition and biodistribution of therapeutic antibodies. AAPS Opn. 2022;8(1):1–18.

Google Scholar 

Igawa T, Tsunoda H, Tachibana T, Maeda A, Mimoto F, Moriyama C, et al. Reduced elimination of IgG antibodies by engineering the variable region. Protein Eng Des Sel. 2010;23(5):385–92. https://doi.org/10.1093/protein/gzq009.

Article  CAS  PubMed  Google Scholar 

Hardiansyah D, Ng CM. Minimal physiologically-based pharmacokinetic model to investigate the effect of charge on the pharmacokinetics of humanized anti-HCV-E2 IgG antibodies in Sprague-Dawley rats. Pharm Res. 2022. https://doi.org/10.1007/s11095-022-03204-2.

Article  PubMed  Google Scholar 

Varkhede N, Forrest ML. Understanding the monoclonal antibody disposition after subcutaneous administration using a minimal physiologically based pharmacokinetic model. J Pharm Pharm Sci. 2018;21(1s):130s–48s. https://doi.org/10.18433/jpps30028.

Article  PubMed  Google Scholar 

Hu S, D’Argenio DZ. Predicting monoclonal antibody pharmacokinetics following subcutaneous administration via whole-body physiologically-based modeling. J Pharmacokinet Pharmacodyn. 2020;47(5):385–409. https://doi.org/10.1007/s10928-020-09691-3.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hu S, Datta-Mannan A, D’Argenio DZ. Physiologically based modeling to predict monoclonal antibody pharmacokinetics in humans from in vitro physiochemical properties. MAbs. 2022;14(1):2056944. https://doi.org/10.1080/19420862.2022.2056944.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hu S, Datta-Mannan A, D’Argenio DZ. Monoclonal antibody pharmacokinetics in cynomolgus monkeys following subcutaneous administration: physiologically based model predictions from physiochemical properties. AAPS J. 2022;25(1):5. https://doi.org/10.1208/s12248-022-00772-4.

Article  CAS  PubMed  Google Scholar 

Jones HM, Zhang Z, Jasper P, Luo H, Avery LB, King LE, et al. A physiologically-based pharmacokinetic model for the prediction of monoclonal antibody pharmacokinetics from in vitro data. CPT Pharmacometrics Syst Pharmacol. 2019;8(10):738–47. https://doi.org/10.1002/psp4.12461.

Article  CAS  PubMed  PubMed Central