A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein‐bound drugs and the impact of errors in plasma protein binding

Abstract

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data was often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding, and blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for terminal elimination half-life (t1/2, 100% of drugs), peak plasma concentration (Cmax, 100%), area under the plasma concentration-time curve (AUC0–t, 95.4%), clearance (CLh, 95.4%), mean retention time (MRT, 95.4%), and steady state volume (Vss, 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model.

On the Nature of Physiologically-Based Pharmacokinetic Models –A Priori or A Posteriori? Mechanistic or Empirical?

“…Since f up is also an important determinant of BP, when calculating tissue partitioning (K p values) from BP, errors in f up will largely cancel (Figure 2). This has been shown previously using a generic PBPK model to simulate the effect of plasma protein binding errors on V ss prediction (13). …”

“…Poulin et al proposed an albumin-mediated transport mechanism that increases the extracellular concentration of drug at the plasma membrane (24), resulting in an increased intracellular concentration and increased clearance. We have recently reported that highly protein bound neutral and acidic drugs require a scaling factor of 12, whereas bases did not require a scaling factor (13). While this is consistent with an albumin-mediated uptake mechanism, in the absence of an active transport or an energy-dependent process, increased concentrations at the membrane cannot increase intracellular concentrations at equilibrium.…”

Accelerated repurposing and drug development of pulmonary hypertension therapies for COVID-19 treatment using an AI-integrated biosimulation platform

“…to validate the ability to predict plasma concentration accurately (as the standard metric for PK). 0.84 3,5,62 3.69* 3.69*…”

An UHPLC-TOF-MS method for quantifying novel brominated anticancer compound bromophenol-thiosemicarbazone hybrid and its application to in vivo pharmacokinetic study

“…In light of, the LLOQ of this method was 13.7 nM, the free concentration in rat plasma expected may be very low based on the generally high plasma protein binding ratio of most of drugs which have high ClogP value. Compared to the agent's inhibitory concentration at 50% of efficacy (IC50) at 29.5 nM against PARP1, which was the free concentration of bromophenol-thiosemicarbazone hybrid, the LLOQ of 13.7 nM may keep sensitive for EC50 studies in vivo (Ye, Nagar et al 2016). In addition, bromophenol-thiosemicarbazone hybrid was expected to develop as an intravenous dosage form according to the results of IC50 in vitro in the future.…”