Impact of Plasma Protein Binding in Drug Clearance Prediction: A Data Base Analysis of Published Studies and Implications for In Vitro-In Vivo Extrapolation

Improving the Translation of Organic Anion Transporting Polypeptide Substrates using HEK293 Cell Data in the Presence and Absence of Human Plasma via Physiologically Based Pharmacokinetic Modeling

“…The results presented here align with previous work examining the hypothesis of proteinfacilitated uptake (Baik and Huang, 2015;Fukuchi et al, 2017;Bowman et al, 2019;Bteich et al, 2019;Kim et al, 2019;Bi et al, 2020;Francis et al, 2020;Liang et al, 2020;Li et al, 2021). According to this idea, interactions between the drug-protein complex and the hepatocyte cell surface or transporters may lead to greater uptake and clearance for highly protein bound drugs (primarily acidic drugs examined to date) than would be predicted using traditional in vitro methods with protein-free buffer.…”

A New Guidance for the Prediction of Hepatic Clearance in the Early Drug Discovery and Development from the in Vitro-to-in Vivo Extrapolation Method and an Approach for Exploring Whether an Albumin-Mediated Hepatic Uptake Phenomenon Could be Present Under in Vivo Conditions

“…Correspondingly, the prediction biases of the extrapolated CL are most probably related to the extrapolation of that available unbound fraction from the in vitro assays to the in vivo condition. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] This aspect has been challenged to explain the reported systematic underestimations biases of the in vivo CL of several drugs bound in plasma. 1,2,[5][6][7] Because there is still no generic IVIVE method that can accurately predict the CL of all drugs in each species, it is essential to pursuit the improvement of the prediction of this parameter, particularly in human.…”

“…1,2,[5][6][7] Because there is still no generic IVIVE method that can accurately predict the CL of all drugs in each species, it is essential to pursuit the improvement of the prediction of this parameter, particularly in human. The degree of accuracy of the published IVIVE methods increased in the last decade, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] but the number of accurate predictions that fall within 2-fold or 3-fold error range compared to the CL in vivo values still needs additional improvements. So, whether the CL int is the metabolic one and/or is combining the metabolism, transporter and permeation limitation activities, it needs to be extrapolated more accurately from the in vitro cells assays to the in vivo condition in the liver.…”

Does Addition of Protein to Hepatocyte or Microsomal In Vitro Incubations Provide a Useful Improvement in In Vitro-In Vivo Extrapolation Predictability?

“…Most recently, Francis et al (2021) also reported increased predictability in the presence of protein for highly protein bound drugs. There are significant differences between the Francis et al (2021) analysis and the material presented here that readers should consider.…”

“…A tendency for significant underprediction for primarily metabolized drugs has been reported (Bowman and Benet, 2016;Wood et al, 2017;Benet and Sodhi, 2020) and for drugs where clearance is rate limited by hepatic uptake (Zou et al, 2013;Kim et al, 2019). Moreover, it has been reported that the predictability of in vivo hepatic clearance is poor for the drugs that are highly bound to protein in blood (Francis et al, 2021). As we (Bowman and Benet, 2018) and Poulin et al (2016) have reviewed, when conducting in vitro studies, many groups have reported that adding plasma or serum to hepatocyte incubations and adding albumin to microsome incubations can cause decreases in free K m values and improved IVIVE results (Ludden et al, 1997;Carlile et al, 1999;Baba et al, 2002;Shibata et al, 2002;Tang et al 2002;Blanchard et al, 2004Blanchard et al, , 2005Blanchard et al, and 2006Rowland et al, 2007Rowland et al, , 2008aRowland et al, and 2008bChao et al, 2009;Wattanachai et al, 2011Wattanachai et al, , 2012Wattanachai et al, and 2015Mao et al, 2012;Walsky et al, 2012;Gill et al, 2012;Palacharila et al, 2017;Fujino et al, 2018;Nishimuta et al, 2019;Kim et al, 2019;Bowman et al, 2019;Bteich et al, 2019) (values presented in Supplementary material, Tables S-1 and S-2).…”