Hepatic clearance concepts and misconceptions: Why the well-stirred model is still used even though it is not physiologic reality?

Furazanopyrazine-based novel promising anticancer agents interfering with the eicosanoid biosynthesis pathways by dual mPGES-1 and sEH inhibition

We report the identification of a new set of compounds based on the furazanopyrazine core interfering with eicosanoid biosynthesis and acting as potentially effective anti-inflammatory and anticancer agents. Based on our previous promising results on a set of furazanopyrazine-based compounds against the microsomal prostaglandin E2 synthase-1 (mPGES-1) enzyme, we here identified derivatives with improved pharmacokinetic properties by replacing the ester moiety with a more stable ether group. A focused virtual library of 1 × 104 molecules was built and screened against mPGES-1 through molecular docking experiments, leading to the selection of 10 candidates for synthesis and biological evaluation. Several molecules were found to inhibit mPGES-1 and, among them, two items featured IC50 values in the low micromolar range. Additional computational studies on the collection of synthesized compounds demonstrated that compound 3b, previously emerged as an mPGES-1 inhibitor, interfered with soluble epoxide hydrolase (sEH) activity, thus emerging as a valuable dual mPGES-1/sEH inhibitor. The pharmacokinetic features of the most potent compounds were accurately estimated. Unfortunately, poor outcomes were obtained for 3b; on the other hand, compound 7e exhibited promising mPGES-1 inhibition and excellent pharmacokinetic profile, demonstrating that the novel furazanopyrazine-based items with ether moiety possess improved pharmacokinetic properties compared to the ester-based compounds reported in our previous study. Additionally, the anticancer properties of 7e and 7d, the latter emerged as the most active mPGES-1 inhibitor, were evaluated and both compounds showed promising activities against HCT-116 human colorectal cancer (CRC) cells. These findings highlight the furazanopyrazine core as a promising scaffold for disclosing new anti-inflammatory drugs with the ability to inhibit targets belonging to arachidonic acid cascade.

Application of Kirchhoff's Laws to pharmacologic and pharmacokinetic analyses

Recently, we introduced a straightforward approach to derive clearance and rate constant equations, without relying on differential equations, utilizing Kirchhoff's Laws, a well known physics methodology used to describe rate-defining processes either in series or parallel. Manuscripts from our laboratory have re-examined published experimental data, demonstrating that the Kirchhoff's Laws methodology can explain data previously considered anomalous, such as the following: (1) all experimental perfused liver clearance data conforming to the equation once thought to represent the unphysiological well stirred model, (2) instances where linear pharmacokinetic systemic bioavailability determinations exceed unity, (3) renal clearance being influenced by drug input processes, (4) statistically significant differences in bioavailability measures between urinary excretion and systemic concentration measurements, and (5) how the long-accepted steady-state clearance approach used in pharmacokinetics for the past half-century leads to unrealistic conclusions about the relationship between liver-to-blood Kpuu and hepatic availability FH. These findings demonstrate the potential for errors in pharmacokinetic evaluations that rely on differential equations. The Kirchhoff's Laws approach is applicable to all pharmacokinetic analyses of quality experimental data, both those that align with present pharmacokinetic theory, and those that do not. Although 3 publications have attempted to rebut our position, they fail to address unexplained experimental data, and we detail here why these analyses are invalid. Our discoveries are ongoing. Additionally, we briefly discuss the application of Kirchoff's Laws to saturable nonlinear kinetics, explaining increased pharmacodynamic response for extended vs immediate release dosage forms, as well as the advantages of successfully formulating high hepatic extraction drugs. SIGNIFICANCE STATEMENT: The Kirchhoff's Laws approach to deriving clearance equations for linear systems in parallel or in series, independent of differential equations, successfully describes anomalous published pharmacokinetic data that have previously been unexplained. We review 9 experimental outcomes in humans that are newly explained using the Kirchhoff's Laws approach, including the extension to deriving nonlinear saturable clearance relationships.

Evaluation of BCRP-Related DDIs Between Methotrexate and Cyclosporin A Using Physiologically Based Pharmacokinetic Modelling

BACKGROUND AND OBJECTIVE

This study provides a physiologically based pharmacokinetic (PBPK) model-based analysis of the potential drug-drug interaction (DDI) between cyclosporin A (CsA), a breast cancer resistance protein transporter (BCRP) inhibitor, and methotrexate (MTX), a putative BCRP substrate.

METHODS

PBPK models for CsA and MTX were built using open-source tools and published data for both model building and for model verification and validation. The MTX and CsA PBPK models were evaluated for their application in simulating BCRP-related DDIs. A qualification of an introduced empirical uniform in vitro scaling factor of Ki values for transporter inhibition by CsA was conducted by using a previously developed model of rosuvastatin (sensitive index BCRP substrate), and assessing if corresponding DDI ratios were well captured.

RESULTS

Within the simulated DDI scenarios for MTX in the presence of CsA, the developed models could capture the observed changes in PK parameters as changes in the area under the curve ratios (area under the curve during DDI/area under the curve control) of 1.30 versus 1.31 observed and the DDI peak plasma concentration ratios (peak plasma concentration during DDI/peak plasma concentration control) of 1.07 versus 1.28 observed. The originally reported in vitro Ki values of CsA were scaled with the uniform qualified scaling factor for their use in the in vivo DDI simulations to correct for the low intracellular unbound fraction of the CsA effector concentration. The resulting predicted versus observed ratios of peak plasma concentration and area under the curve DDI ratios with MTX were 0.82 and 0.99, respectively, indicating adequate model accuracy and choice of a scaling factor to capture the observed DDI.

CONCLUSIONS

All models have been comprehensively documented and made publicly available as tools to support the drug development and clinical research community and further community-driven model development.

Physiologically based pharmacokinetic modeling of small molecules: How much progress have we made?

Physiologically based pharmacokinetic (PBPK) models of small molecules have become mainstream in drug development and in academic research. The use of PBPK models is continuously expanding, with the majority of work now focusing on predictions of drug-drug interactions, drug-disease interactions, and changes in drug disposition across lifespan. Recently, publications that use PBPK modeling to predict drug disposition during pregnancy and in organ impairment have increased reflecting the advances in incorporating diverse physiologic changes into the models. Because of the expanding computational power and diversity of modeling platforms available, the complexity of PBPK models has also increased. Academic efforts have provided clear advances in better capturing human physiology in PBPK models and incorporating more complex mathematical concepts into PBPK models. Examples of such advances include the segregated gut model with a series of gut compartments allowing modeling of physiologic blood flow distribution within an organ and zonation of metabolic enzymes and series compartment liver models allowing simulations of hepatic clearance for high extraction drugs. Despite these advances in academic research, the progress in assessing model quality and defining model acceptance criteria based on the intended use of the models has not kept pace. This Minireview suggests that awareness of the need for predefined criteria for model acceptance has increased, but many manuscripts still lack description of scientific justification and/or rationale for chosen acceptance criteria. As artificial intelligence and machine learning approaches become more broadly accepted, these tools offer promise for development of comprehensive assessment for existing observed data and analysis of model performance. SIGNIFICANCE STATEMENT: Physiologically based pharmacokinetic (PBPK) modeling has become a mainstream application in academic literature and is broadly used for predictions, analysis, and evaluation of pharmacokinetic data. Significant progress has been made in developing advanced PBPK models that better capture human physiology, but oftentimes sufficient justification for the chosen model acceptance criterion and model structure is still missing. This Minireview provides a summary of the current landscape of PBPK applications used and highlights the need for advancing PBPK modeling science and training in academia.

Predicting Clearance with Simple and Permeability-Limited Physiologically Based Pharmacokinetic Frameworks: Comparison of Well-Stirred, Dispersion, and Parallel-Tube Liver Models

One-compartment (1C) and permeability-limited models were used to evaluate the ability of microsomal and hepatocyte intrinsic clearances to predict hepatic clearance. Well-stirred (WSM), parallel-tube (PTM), and dispersion (DM) models were evaluated within the liver as well as within whole-body physiologically based pharmacokinetic frameworks. It was shown that a linear combination of well-stirred and parallel-tube average liver blood concentrations accurately approximates dispersion model blood concentrations. Using a flow/permeability-limited model, a large systematic error was observed for acids and no systematic error for bases. A scaling factor that reduced interstitial fluid (ISF) plasma protein binding could greatly decrease the absolute average fold error (AAFE) for acids. Using a 1C model, a scalar to reduce plasma protein binding decreased the microsomal clearance AAFE for both acids and bases. With a permeability-limited model, only acids required this scalar. The mechanism of the apparent increased cytosolic concentrations for acids remains unknown. We also show that for hepatocyte intrinsic clearance in vitro-in vivo correlations (IVIVCs), a 1C model is mechanistically appropriate since hepatocyte clearance should represent the net clearance from ISF to elimination. A relationship was derived that uses microsomal and hepatocyte intrinsic clearance to solve for an active hepatic uptake clearance, but the results were inconclusive. Finally, the PTM model generally performed better than the WSM or DM models, with no clear advantage between microsomes and hepatocytes. SIGNIFICANCE STATEMENT: Prediction of drug clearance from microsomes or hepatocytes remains challenging. Various liver models (e.g., well-stirred, parallel-tube, and dispersion) have been mathematically incorporated into liver as well as whole-body physiologically based pharmacokinetic frameworks. Although the resulting models allow incorporation of pH partitioning, permeability, and active uptake for prediction of drug clearance, including these processes did not improve clearance predictions for both microsomes and hepatocytes.

How predictive are isolated perfused liver data of in vivo hepatic clearance? A meta-analysis of isolated perfused rat liver data

Isolated perfused rat liver (IPRL) experiments have been used to answer clearance-related questions, including evaluating the impact of pathological and physiological processes on hepatic clearance (CLH). However, to date, IPRL data has not been evaluated for in vivo CLH prediction accuracy.In addition to a detailed overview of available IPRL literature, we present an in-depth analysis of the performance of IPRL in CLH prediction.While the entire dataset poorly predicted CLH (GAFE = 3.2; 64% within 3-fold), IPRL conducted under optimal experimental conditions, such as in the presence of plasma proteins and with a perfusion rate within 2-fold of physiological liver blood flow and corrected for unbound fraction in the presence of red blood cells, can accurately predict rat CLH (GAFE = 2.0; 78% within 3-fold). Careful consideration of experimental conditions is needed to allow proper data analysis.Further, isolated perfused liver experiments in other species, including human livers, may allow us to address the current in vitro-in vivo disconnects of hepatic metabolic clearance and improve our methodology for CLH predictions.

Commentary: Pharmacokinetic Theory Must Consider Published Experimental Data

Recently, we have proposed simple methodology to derive clearance and rate constant equations, independent of differential equations, based on Kirchhoff's Laws, a common methodology from physics used to describe rate-defining processes either in series or parallel. Our approach has been challenged in three recent publications, two published in this journal, but notably what is lacking is that none evaluate experimental pharmacokinetic data. As reviewed here, manuscripts from our laboratory have evaluated published experimental data, demonstrating that the Kirchhoff's Laws approach explains (1) why all of the experimental perfused liver clearance data appear to fit the equation that was previously believed to be the well-stirred model, (2) why linear pharmacokinetic systemic bioavailability determinations can be greater than 1, (3) why renal clearance can be a function of drug input processes, and (4) why statistically different bioavailability measures may be found for urinary excretion versus systemic concentration measurements. Our most recent paper demonstrates (5) how the universally accepted steady-state clearance approach used by the field for the past 50 years leads to unrealistic outcomes concerning the relationship between liver-to-blood Kpuu and hepatic availability FH , highlighting the potential for errors in pharmacokinetic evaluations based on differential equations. The Kirchhoff's Laws approach is applicable to all pharmacokinetic analyses of quality experimental data, those that were previously adequately explained with present pharmacokinetic theory, and those that were not. The publications that have attempted to rebut our position do not address unexplained experimental data, and we show here why their analyses are not valid. SIGNIFICANCE STATEMENT: The Kirchhoff's Laws approach to deriving clearance equations for linear systems in parallel or in series, independent of differential equations, successfully describes published pharmacokinetic data that has previously been unexplained. Three recent publications claim to refute our proposed methodology; these publications only make theoretical arguments, do not evaluate experimental data, and never demonstrate that the Kirchhoff methodology provides incorrect interpretations of experimental pharmacokinetic data, including statistically significant data not explained by present pharmacokinetic theory. We demonstrate why these analyses are invalid.

Quantitative prediction of CYP3A induction-mediated drug-drug interactions in clinical practice

There have been no reports on the quantitative prediction of CYP3A induction-mediated decreases in AUC and Cmax for drug candidates identified as a "victims" of CYP3A induction. Our previous study separately evaluated the fold-induction of hepatic and intestinal CYP3A by known inducers using clinical induction data and revealed that we were able to quantitatively predict the AUC ratio (AUCR) of a few CYP3A substrates in the presence and absence of CYP3A inducers. In the present study, we investigate the predictability of AUCR and also Cmax ratio (CmaxR) in additional 54 clinical studies. The fraction metabolized by CYP3A (fm), the intestinal bioavailability (Fg), and the hepatic intrinsic clearance (CLint) of substrates were determined by the in vitro experiments as well as clinical data used for calculating AUCR and CmaxR. The result showed that 65-69% and 65-67% of predictions were within 2-fold of observed AUCR and CmaxR, respectively. A simulation using multiple parameter combinations suggested that the variability of fm and Fg within a certain range might have a minimal impact on the calculation output. These findings suggest that clinical AUCR and CmaxR of CYP3A substrates can be quantitatively predicted from the preclinical stage.

After 50 Years of Hepatic Clearance Models, Where Should We Go from Here? Improvements and Implications for Physiologically Based Pharmacokinetic Modeling

There is overwhelming preference for application of the unphysiologic, well-stirred model (WSM) over the parallel tube model (PTM) and dispersion model (DM) to predict hepatic drug clearance, CLH , despite that liver blood flow is dispersive and closer to the DM in nature. The reasoning is the ease in computation relating the hepatic intrinsic clearance ( CLint ), hepatic blood flow ( QH ), unbound fraction in blood ( fub ) and the transmembrane clearances ( CLin and CLef ) to CLH for the WSM. However, the WSM, being the least efficient liver model, predicts a lower EH that is associated with the in vitro CLint ( Vmax / Km ), therefore requiring scale-up to predict CLH in vivo. By contrast, the miniPTM, a three-subcompartment tank-in-series model of uniform enzymes, closely mimics the DM and yielded similar patterns for CLint versus EH , substrate concentration [S] , and KL / B , the tissue to outflow blood concentration ratio. We placed these liver models nested within physiologically based pharmacokinetic models to describe the kinetics of the flow-limited, phenolic substrate, harmol, using the WSM (single compartment) and the miniPTM and zonal liver models (ZLMs) of evenly and unevenly distributed glucuronidation and sulfation activities, respectively, to predict CLH For the same, given CLint ( Vmax and Km ), the WSM again furnished the lowest extraction ratio ( EH,WSM = 0.5) compared with the miniPTM and ZLM (>0.68). Values of EH,WSM were elevated to those for EH, PTM and EH, ZLM when the Vmax s for sulfation and glucuronidation were raised 5.7- to 1.15-fold. The miniPTM is easily manageable mathematically and should be the new normal for liver/physiologic modeling. SIGNIFICANCE STATEMENT: Selection of the proper liver clearance model impacts strongly on CLH predictions. The authors recommend use of the tank-in-series miniPTM (3 compartments mini-parallel tube model), which displays similar properties as the dispersion model (DM) in relating CLint and [ S ] to CLH as a stand-in for the DM, which best describes the liver microcirculation. The miniPTM is readily modified to accommodate enzyme and transporter zonation.

Transporter-mediated drug-drug interactions: regulatory guidelines, in vitro and in vivo methodologies and translation, special populations, and the blood-brain barrier

This review, part of a special issue on drug-drug interactions (DDIs) spearheaded by the International Society for the Study of Xenobiotics (ISSX) New Investigators, explores the critical role of drug transporters in absorption, disposition, and clearance in the context of DDIs. Over the past two decades, significant advances have been made in understanding the clinical relevance of these transporters. Current knowledge on key uptake and efflux transporters that affect drug disposition and development is summarized. Regulatory guidelines from the FDA, EMA, and PMDA that inform the evaluation of potential transporter-mediated DDIs are discussed in detail. Methodologies for preclinical and clinical testing to assess potential DDIs are reviewed, with an emphasis on the utility of physiologically based pharmacokinetic (PBPK) modeling. This includes the application of relative abundance and expression factors to predict human pharmacokinetics (PK) using preclinical data, integrating the latest regulatory guidelines. Considerations for assessing transporter-mediated DDIs in special populations, including pediatric, hepatic, and renal impairment groups, are provided. Additionally, the impact of transporters at the blood-brain barrier (BBB) on the disposition of CNS-related drugs is explored. Enhancing the understanding of drug transporters and their role in drug disposition and toxicity can improve efficacy and reduce adverse effects. Continued research is essential to bridge remaining gaps in knowledge, particularly in comparison with cytochrome P450 (CYP) enzymes.

In Silico Assisted Identification, Synthesis, and In Vitro Pharmacological Characterization of Potent and Selective Blockers of the Epilepsy-Associated KCNT1 Channel

Gain-of-function (GoF) variants in KCNT1 channels cause severe, drug-resistant forms of epilepsy. Quinidine is a known KCNT1 blocker, but its clinical use is limited due to severe drawbacks. To identify novel KCNT1 blockers, a homology model of human KCNT1 was built and used to screen an in-house library of compounds. Among the 20 molecules selected, five (CPK4, 13, 16, 18, and 20) showed strong KCNT1-blocking ability in an in vitro fluorescence-based assay. Patch-clamp experiments confirmed a higher KCNT1-blocking potency of these compounds when compared to quinidine, and their selectivity for KCNT1 over hERG and Kv7.2 channels. Among identified molecules, CPK20 displayed the highest metabolic stability; this compound also blocked KCNT2 currents, although with a lower potency, and counteracted GoF effects prompted by 2 recurrent epilepsy-causing KCNT1 variants (G288S and A934T). The present results provide solid rational basis for future design of novel compounds to counteract KCNT1-related neurological disorders.

Are all measures of liver Kpuu a function of FH, as determined following oral dosing, or have we made a critical error in defining hepatic drug clearance?

Here we present, utilizing universally accepted relationships for hepatic clearance at steady state, that for all models of hepatic elimination the ratio of unbound liver drug concentration to unbound systemic blood concentration, Kpuu, is a function of or related to the hepatic bioavailability for that drug, FH. According to the derivation for the well-stirred model, Kpuu can never exceed unity, can frequently be a function of hepatic blood flow, and is equivalent to the value of FH as determined following oral dosing. For the parallel tube model, Kpuu will not equal FH but will be a function of FH and will also never be a value greater than 1. When hepatic clearance is rate limited by basolateral transporters, Kpuu will be less than 1, and less than FH. We believe that such outcomes are highly unlikely, and that the error arises from a basic assumption concerning hepatic clearance that leads to the mechanistic models of hepatic elimination, the well-stirred, parallel tube and dispersion models. That basic assumption is that the steady-state systemic concentration multiplied by the hepatic systemic clearance is equal to the product of the average unbound liver steady-state concentration and the intrinsic hepatic clearance (Css · CL = CH,u · CLint). Calculations of Kpuu and FH based on present methods of analysis provide a strong argument as to why this universally accepted relationship is not correct. Alternatively, we have shown in recent publications that hepatic clearance may be adequately determined based on Kirchhoff's Laws where no assumption of the above equality concerning hepatic intrinsic clearance is required, and where Kpuu is independent of hepatic extraction ratio and FH.

Introducing the Dynamic Well-Stirred Model for Predicting Hepatic Clearance and Extraction Ratio

The well-stirred model (WSM) incorporating the fraction of unbound drug (fu) to account for the effect of plasma binding on intrinsic clearance has been widely used for predicting hepatic clearance under the assumption that drug protein binding reaches equilibrium instantaneously. Our theoretical analysis reveals that the effect of protein binding on intrinsic clearance is better accounted for with the dynamic free fraction (fD), a measure of drug protein binding affinity, which leads to a putative dynamic well-stirred model (dWSM) without the instantaneous equilibrium assumption. Using recombinant CYP3A4 as the in vitro clearance system, we demonstrate that the binding effect of albumin on the intrinsic clearance of both highly bound midazolam and highly free verapamil is fully corrected by their corresponding fD values, respectively. On the other hand, fu only corrects the binding effect of albumin on the intrinsic clearance of verapamil, and yields severe over-correction of the intrinsic clearance of midazolam. The results suggest that the traditional WSM is suitable for highly free drugs like verapamil but not necessarily for highly bound drugs such as midazolam due to the violation of the instantaneous equilibrium assumption or under-estimating the true free drug concentration. In comparison, the dWSM incorporating fD holds true as long as drug elimination follows steady-state kinetics, and hence, it is more broadly applicable to drugs with different protein binding characteristics. Here we demonstrate with 36 diverse drugs, that the dWSM significantly improves the accuracy of predicting human hepatic clearance and liver extraction ratio from in vitro microsomal clearance data, highlighting the importance of drug plasma protein binding kinetics in addressing the under-prediction of hepatic clearance by the WSM.

Peptidomimetics as potent dual SARS-CoV-2 cathepsin-L and main protease inhibitors: In silico design, synthesis and pharmacological characterization

In this paper we present the design, synthesis, and biological evaluation of a new series of peptidomimetics acting as potent anti-SARS-CoV-2 agents. Starting from our previously described Main Protease (MPro) and Papain Like Protease (PLPro) dual inhibitor, CV11, here we disclose its high inhibitory activity against cathepsin L (CTSL) (IC50 = 19.80 ± 4.44 nM), an emerging target in SARS-CoV-2 infection machinery. An in silico design, inspired by the structure of CV11, led to the development of a library of peptidomimetics showing interesting activities against CTSL and Mpro, allowing us to trace the chemical requirements for the binding to both enzymes. The screening in Vero cells infected with 5 different SARS-CoV-2 variants of concerns, highlighted sub-micromolar activities for most of the synthesized compounds (13, 15, 16, 17 and 31) in agreement with the enzymatic inhibition assays results. The compounds showed lack of activity against several different RNA viruses except for the 229E and OC43 human coronavirus strains, also characterized by a cathepsin-L dependent release into the host cells. The most promising derivatives were also evaluated for their chemical and metabolic in-vitro stability, with derivatives 15 and 17 showing a suitable profile for further preclinical characterization.

A Complete Extension of Classical Hepatic Clearance Models Using Fractional Distribution Parameter fd in Physiologically Based Pharmacokinetics

The classical organ clearance models have been proposed to relate the plasma clearance CLp to probable mechanism(s) of hepatic clearance. However, the classical models assume the intrinsic capability of drug elimination (CLu,int) that is physically segregated from the vascular blood but directly acts upon the unbound drug concentration in the blood (fubCavg), and do not handle the transit-time delay between the inlet/outlet concentrations in their closed-form clearance equations. Therefore, we propose unified model structures that can address the internal blood concentration patterns of clearance organs in a more mechanistic/physiological manner, based on the fractional distribution parameter fd operative in PBPK. The basic partial/ordinary differential equations for four classical models are revisited/modified to yield a more complete set of extended clearance models, i.e., the Rattle, Sieve, Tube, and Jar models, which are the counterparts of the dispersion, series-compartment, parallel-tube, and well-stirred models. We demonstrate the feasibility of applying the resulting extended models to isolated perfused rat liver data for 11 compounds and an example dataset for in vitro-in vivo extrapolation of the intrinsic to the systemic clearances. Based on their feasibilities to handle such real data, these models may serve as an improved basis for applying clearance models in the future.

Kirchhoff's Laws and Hepatic Clearance, Well-Stirred Model - Is There Common Ground?

Clearance concepts are extensively applied in drug development and drug therapy. The well-stirred model (WSM) of hepatic elimination is the most widely adopted physiologic model in pharmacokinetics owing to its simplicity. A common feature of this organ model is its use to relate hepatic clearance of a compound to the physiologic variables: organ blood flow rate, binding within blood, and hepatocellular metabolic and excretory activities. Recently, Kirchhoff's laws of electrical network have been applied to organ clearance (Pachter et al., 2022; Benet and Sodhi, 2023) with the claim that they yield the same equation for hepatic clearance as the WSM, and that the equation is independent of a mechanistic model. This commentary analyzes this claim and shows that implicit in the application of Kirchhoff's approaches are the same assumptions as those of the WSM. Concern is also expressed in the interpretation of permeability or transport parameters and related equations, as well as the inappropriateness of the corresponding equation defining hepatic clearance. There is no value, and some dangers, in applying Kirchhoff's electrical laws to organ clearance. SIGNIFICANCE STATEMENT: This commentary refutes this claim by Pachter et al. (2022), and Benet and Sodhi, (2023), who suggest that the well-stirred model (WSM) of hepatic elimination, the most widely applied physiologic model of hepatic clearance, provides the same equation as Kirchhoff's laws of electrical network that is independent of a physiologic model. A careful review shows that the claim is groundless and fraught with errors. We conclude that there is no place for the application of Kirchhoff's laws to organ clearance models.

Drug Design and Success of Prospective Mouse In Vitro-In Vivo Extrapolation (IVIVE) for Predictions of Plasma Clearance (CLp) from Hepatocyte Intrinsic Clearance (CLint)

Hepatocyte intrinsic clearance (CL_int) and methods of in vitro-in vivo extrapolation (IVIVE) are often used to predict plasma clearance (CL_p) in drug discovery. While the prediction success of this approach is dependent on the chemotype, specific molecular properties and drug design features that govern these outcomes are poorly understood. To address this challenge, we investigated the success of prospective mouse CL_p IVIVE across 2142 chemically diverse compounds. Dilution scaling, which assumes that the free fraction in hepatocyte incubations (f_u,inc) is governed by binding to the 10% of serum in the incubation medium, was used as our default CL_p IVIVE approach. Results show that predictions of CL_p are better for smaller (molecular weight (MW) < 500 Da), less polar (total polar surface area (TPSA) < 100 Ų, hydrogen bond donor (HBD) ≤1, hydrogen bond acceptor (HBA) ≤ 6), lipophilic (log D > 3), and neutral compounds, with low HBD count playing the key role. If compounds are classified according to their chemical space, predictions were good for compounds resembling central nervous system (CNS) drugs [average absolute fold error (AAFE) of 2.05, average fold error (AFE) of 0.90], moderate for classical druglike compounds (according to Lipinski, Veber, and Ghose guidelines; AAFE of 2.55; AFE of 0.68), and poor for nonclassical "beyond the rule of 5" compounds (AAFE of 3.31; AFE of 0.41). From the perspective of measured druglike properties, predictions of CL_p were better for compounds with moderate-to-high hepatocyte CL_int (>10 μL/min/10⁶ cells), high passive cellular permeability (P_app > 100 nm/s), and moderate observed CL_p (5-50 mL/min/kg). Influences of plasma protein binding (f_u,p) and P-glycoprotein (Pgp) apical efflux ratio (AP-ER) were less pronounced. If the extended clearance classification system (ECCS) is applied, predictions were good for class 2 (P_app > 50 nm/s; neutral or basic; AAFE of 2.35; AFE of 0.70) and acceptable for class 1A compounds (AAFE of 2.98; AFE of 0.70). Classes 1B, 3 A/B, and 4 showed poor outcomes (AAFE > 3.80; AFE < 0.60). Functional groups trending toward weaker CL_p IVIVE were esters, carbamates, sulfonamides, carboxylic acids, ketones, primary and secondary amines, primary alcohols, oxetanes, and compounds liable to aldehyde oxidase metabolism, likely due to multifactorial reasons. Multivariate analysis showed that multiple properties are relevant, combining together to define the overall success of CL_p IVIVE. Our results indicate that the current practice of prospective CL_p IVIVE is suitable only for CNS-like compounds and well-behaved classical druglike space (e.g., high permeability or ECCS class 2) without challenging functional groups. Unfortunately, based on existing mouse data, prospective CL_p IVIVE for complex and nonclassical chemotypes is poor and hardly better than random guessing. This is likely due to complexities such as extrahepatic metabolism and transporter-mediated disposition which are poorly captured by this methodology. With small-molecule drug discovery increasingly evolving toward nonclassical and complex chemotypes, existing CL_p IVIVE methodology will require improvement. While empirical correction factors may bridge the gap in the near future, improved and new in vitro assays, data integration models, and machine learning (ML) methods are increasingly needed to address this challenge and reduce the number of nonclinical pharmacokinetic (PK) studies.

Exploring the Pharmacokinetic Mysteries of the Liver: Application of Series Compartment Models of Hepatic Elimination

Among the basic hepatic clearance models, the dispersion model (DM) is the most physiologically sound compared with the well-stirred model and the parallel tube model. However, its application in physiologically-based pharmacokinetic (PBPK) modeling has been limited due to computational complexities. The series compartment models (SCM) of hepatic elimination that treats the liver as a cascade of well-stirred compartments connected by hepatic blood flow exhibits some mathematical similarities to the DM but is easier to operate. This work assesses the quantitative correlation between the SCM and DM and demonstrates the operation of the SCM in PBPK with the published single-dose blood and liver concentration-time data of six flow-limited compounds. The predicted liver concentrations and the estimated intrinsic clearance (CLint ) and PBPK-operative tissue-to-plasma partition coefficient (Kp ) values were shown to depend on the number of liver sub-compartments (n) and hepatic enzyme zonation in the SCM. The CLint and Kp decreased with increasing n, with more remarkable differences for drugs with higher hepatic extraction ratios. Given the same total CLint , the SCM yields a higher Kp when the liver perivenous region exhibits a lower CLint as compared with a high CLint at this region. Overall, the SCM nicely approximates the DM in characterizing hepatic elimination and offers an alternative flexible approach as well as providing some insights regarding sequential drug concentrations in the liver. SIGNIFICANCE STATEMENT: The SCM nicely approximates the DM when applied in PBPK for characterizing hepatic elimination. The number of liver sub-compartments and hepatic enzyme zonation are influencing factors for the SCM resulting in model-dependent predictions of total/internal liver concentrations and estimates of CLint and the PBPK-operative Kp . Such model-dependency may have an impact when the SCM is used for in vitro-to-in vivo extrapolation (IVIVE) and may also be relevant for PK/PD/toxicological effects when it is the driving force for such responses.

Process and System Clearances in Pharmacokinetic Models: Our Basic Clearance Concepts Are Correct

Clearances are important parameters in pharmacokinetic (PK) models. All clearances in PK models are either process clearances that include diffusion, transport, and metabolism clearances or system clearances that include organ and systemic clearance. Clearance and volume of distribution are two independent parameters that characterize drug disposition in both individual compartments and systems of compartments. In this minireview, we show that systemic and organ clearances are net clearances that can be easily derived by partition analysis. When drugs are eliminated from the central compartment by first-order processes, systemic clearance is constant. When drugs are eliminated from a peripheral compartment, instantaneous systemic clearance will vary with time. However, average clearance and clearance at steady state will be constant and will equal dose divided by area under the curve. We show that peripheral elimination will not have a large impact on most pharmacokinetic analyses and that standard models of organ and systemic clearance are useful and appropriate. SIGNIFICANCE STATEMENT: There are two basic kinds of clearances used in pharmacokinetic models, process and system clearances. We show that organ and systemic clearances are net clearances with blood or plasma as the driving concentration. For linear pharmacokinetics, clearance is constant for elimination from the central compartment but varies with time for peripheral elimination. Despite the different kinds of clearance parameters and models, standard clearance models and concepts remain valid.

Plasma Protein-Mediated Uptake and Contradictions to the Free Drug Hypothesis: A Critical Review

According to the free drug hypothesis (FDH), only free, unbound drug is available to interact with biological targets. This hypothesis is the fundamental principle that continues to explain the vast majority of all pharmacokinetic and pharmacodynamic processes. Under the FDH, the free drug concentration at the target site is considered the driver of pharmacodynamic activity and pharmacokinetic processes. However, deviations from the FDH are observed in hepatic uptake and clearance predictions, where observed unbound intrinsic hepatic clearance (CL_int,u) is larger than expected. Such deviations are commonly observed when plasma proteins are present and form the basis of the so-called plasma protein-mediated uptake effect (PMUE). This review will discuss the basis of plasma protein binding as it pertains to hepatic clearance based on the FDH, as well as several hypotheses that may explain the underlying mechanisms of PMUE. Notably, some, but not all, potential mechanisms remained aligned with the FDH. Finally, we will outline possible experimental strategies to elucidate PMUE mechanisms. Understanding the mechanisms of PMUE and its potential contribution to clearance underprediction is vital to improving the drug development process.

Simplifying the Extended Clearance Concept Classification System (EC3S) to Guide Clearance Prediction in Drug Discovery

PURPOSE

The Extended Clearance Concept Classification System was established as a development-stage tool to provide a framework for identifying fundamental mechanism(s) governing drug disposition in humans. In the present study, the applicability of the EC3S in drug discovery has been investigated. In its current format, the EC3S relies on low-throughput hepatocyte uptake data, which are not frequently generated in a discovery setting.

METHODS

A relationship between hepatocyte uptake clearance and MDCK permeability was first established along with intrinsic clearance from human liver microsomes. The performance of this approach was examined by categorizing 64 drugs into EC3S classes and comparing the predicted major elimination pathway(s) to that observed in humans. As an extension of the work, the ability of the simplified EC3S to predict human systemic clearance based on intrinsic clearance generated using in-vitro metabolic systems was evaluated.

RESULTS

The assessment enabled the use of MDCK permeability and unscaled unbound intrinsic clearance to generate cut-off criteria to categorize compounds into four EC3S classes: Class 12ab, 2cd, 34ab, and 34cd, with major elimination mechanism(s) assigned to each class. The predictivity analysis suggested that systemic clearance could generally be predicted within threefold for EC3S class 12ab and 34ab compounds. For classes 2cd and 34cd, systemic clearance was poorly predicted using in-vitro systems explored in this study.

CONCLUSION

Collectively, our simplified classification approach is expected to facilitate the identification of mechanism(s) involved in drug elimination, faster resolution of in-vitro to in-vivo disconnects, and better design of mechanistic pharmacokinetic studies in drug discovery.

Investigation of simplified physiologically-based pharmacokinetic models in rat and human

Whole-body physiologically-based pharmacokinetic (PBPK) models have many applications in drug research and development. It is often necessary to inform these models with animal or clinical data, updating model parameters, and making the model more predictive for future applications. This provides an opportunity and a challenge given the large number of parameters of such models. The aim of this work was to propose new mechanistic model structures with reduced complexity allowing for parameter optimization. These models were evaluated for their ability to estimate realistic values for unbound tissue to plasma partition coefficients (Kpu) and simulate observed pharmacokinetic (PK) data. Two approaches are presented: using either established kinetic lumping methods based on tissue time constants (drug-dependent) or a novel clustering analysis to identify tissues sharing common Kpu values or Kpu scalars based on similarities of tissue composition (drug-independent). PBPK models derived from these approaches were assessed using PK data of diazepam in rats and humans. Although the clustering analysis produced minor differences in tissue grouping depending on the method used, two larger groups of tissues emerged. One including the kidneys, liver, spleen, gut, heart, and lungs, and another including bone, brain, muscle, and pancreas whereas adipose and skin were generally considered distinct. Overall, a subdivision into four tissue groups appeared most physiologically relevant in terms of tissue composition. Several models were found to have similar abilities to describe diazepam i.v. data as empirical models. Comparability of estimated Kpus to experimental Kpu values for diazepam was one criterion for selecting the appropriate PK model structure.

Considering the Oral Bioavailability of Protein Kinase Inhibitors: Essential in Assessing the Extent of Drug-Drug Interaction and Improving Clinical Practice

Protein kinase inhibitors share pharmacokinetic (PK) pathways among themselves. They are all metabolized by several cytochromes P450 (CYP). For most of them, CYP3A4 is the predominant metabolic pathway. However, their oral bioavailability differs. For example, the oral bioavailability of imatinib has been estimated at nearly 100%, but that of ibrutinib averages 3% due to its high hepatic first-pass effect. Overall, the smaller the oral bioavailability, the larger its interindividual PK variability. Indeed, for drugs with low oral bioavailability, the extent of their absorption is an additional cause (along with elimination variability) of differences in drug exposure among patients. The impact of drug-drug interaction (DDI) also differs between drugs with low or high oral bioavailability. We describe and explain why the impact of CYP3A4 inhibitors and inducers is much greater for protein kinase inhibitors with low oral bioavailability. The effect of food on protein kinase inhibitors and DDIs corresponding to plasma protein binding will also be considered. Finally, the benefits of these concepts in clinical practice (including therapeutic drug monitoring) will be discussed. Overall, our main objective was to apply fundamental PK concepts to understanding the main clinical issues of these oral anticancer drugs.

Predicting changes in the pharmacokinetics of CYP3A-metabolized drugs in hepatic impairment and insights into factors driving these changes

Physiologically based pharmacokinetic models, populated with drug-metabolizing enzyme and transporter (DMET) abundance, can be used to predict the impact of hepatic impairment (HI) on the pharmacokinetics (PK) of drugs. To increase confidence in the predictive power of such models, they must be validated by comparing the predicted and observed PK of drugs in HI obtained by phenotyping (or probe drug) studies. Therefore, we first predicted the effect of all stages of HI (mild to severe) on the PK of drugs primarily metabolized by cytochrome P450 (CYP) 3A enzymes using the default HI module of Simcyp Version 21, populated with hepatic and intestinal CYP3A abundance data. Then, we validated the predictions using CYP3A probe drug phenotyping studies conducted in HI. Seven CYP3A substrates, metabolized primarily via CYP3A (fraction metabolized, 0.7-0.95), with low to high hepatic availability, were studied. For all stages of HI, the predicted PK parameters of drugs were within twofold of the observed data. This successful validation increases confidence in using the DMET abundance data in HI to predict the changes in the PK of drugs cleared by DMET for which phenotyping studies in HI are not available or cannot be conducted. In addition, using CYP3A drugs as an example, through simulations, we identified the salient PK factors that drive the major changes in exposure (area under the plasma concentration-time profile curve) to drugs in HI. This theoretical framework can be applied to any drug and DMET to quickly determine the likely magnitude of change in drug PK due to HI.

Review of the application of Kirchhoff's Laws of series and parallel flows to pharmacology: Defining organ clearance

Dosing rate decisions for drugs and changes in dosing in a patient due to disease states, drug interactions and pharmacogenomics are all based on clearance, a measure of the body's ability to eliminate drug. The primary organs of elimination are the liver and the kidney. Clearance for each of these organs is a summative composition of biologic processes. In 1857, Gustav Kirchhoff first developed his laws to describe the "motion of electricity in conductors... [and] ...in wires", recognizing that summative processes occur either in parallel or in series. Since then, Kirchhoff's Laws have also been applied to heat transfer, diffusion and drag force on falling objects, but not to pharmacology. Although not previously recognized, renal clearance always follow Kirchhoff's Laws, as does hepatic clearance for drugs where basolateral transporters are not clinically relevant. However, when basolateral transporters are clinically relevant, we demonstrate that the present accepted approach is inconsistent with recognized drug disposition processes. However, this clearance relationship can be easily corrected using Kirchhoff's Laws. The purpose of this review is to demonstrate that Kirchhoff's Laws, which define how to approach rate processes that occur in parallel versus processes that occur in series, can be applicable to pharmacology in addition to the over 160-year recognition of their use in physical sciences. We anticipate that the application to clearance will be only the first of many such pharmacological analyses.

Examining Physiologically Based Pharmacokinetic Model Assumptions for Cross-Tissue Similarity of Activity per Unit of Enzyme: The Case Example of Uridine 5'-Diphosphate Glucuronosyltransferase

The default assumption during in vitro in vivo extrapolation (IVIVE) to predict metabolic clearance in physiologically based pharmacokinetics (PBPK) is that protein expression and activity have the same relationship in various tissues. This assumption is examined for uridine 5'-diphosphate glucuronosyltransferases (UGTs), a case example where expression and hence metabolic activity are distributed across various tissues. Our literature analysis presents overwhelming evidence of a greater UGT activity per unit of enzyme (higher kcat) in kidney and intestinal tissues relative to liver (greater than 200-fold for UGT2B7). This analysis is based on application of abundance values reported using similar proteomic techniques and within the same laboratory. Our findings call into question the practice of assuming similar kcat during IVIVE estimations as part of PBPK and call for a systematic assessment of the kcat of various enzymes across different organs. The analysis focused on compiling data for probe substrates that were common for two or more of the studied tissues to allow for reliable comparison of cross-tissue enzyme kinetics; this meant that UGT enzymes included in the study were limited to UGT1A1, 1A3, 1A6, 1A9, and 2B7. Significantly, UGT1A9 (n = 24) and the liver (n = 27) were each found to account for around half of the total dataset; these were found to correlate with hepatic UGT1A9 data found in 15 of the studies, highlighting the need for more research into extrahepatic tissues and other UGT isoforms. SIGNIFICANCE STATEMENT: During physiologically based pharmacokinetic modeling (in vitro in vivo extrapolation) of drug clearance, the default assumption is that the activity per unit of enzyme is the same in all tissues. The analysis provides preliminary evidence that this may not be the case and that renal and intestinal tissues may have almost 250-fold greater uridine 5'-diphosphate glucuronosyltransferase activity per unit of enzyme than liver tissues.

Sinusoidal Uptake Determines the Hepatic Clearance of Pevonedistat (TAK-924) as Explained by Extended Clearance Model

Quantitative assessment of hepatic clearance (CLH) of drugs is critical to accurately predict human dose and drug-drug interaction (DDI) liabilities. This is challenging for drugs that involve complex transporter-enzyme interplay. In this study, we demonstrate this interplay in the CLH and DDI effect in the presence of CYP3A4 perpetrator for pevonedistat using both the conventional clearance model (CCM) and the extended clearance model (ECM). In vitro metabolism and hepatocyte uptake data showed that pevonedistat is actively transported into the liver via multiple uptake transporters and metabolized predominantly by CYP3A4 (88%). The active uptake clearance (CLact,inf) and passive diffusion clearance (CLdiff,inf) were 21 and 8.7 ml/min/kg, respectively. The CLact,inf was underpredicted as Empirical Scaling Factor of 13 was needed to recover the in vivo plasma clearance (CLplasma). Both CCM and ECM predicted CLplasma of pevonedistat reasonably well (predicted CLplasma of 30.8 (CCM) and 32.1 (ECM) versus observed CLplasma of 32.2 ml/min/kg). However, both systemic and liver exposures in the presence of itraconazole were well predicted by ECM but not by CCM (predicted pevonedistat plasma area under the concentration-time curve ratio (AUCR) 2.73 (CCM) and 1.23 (ECM))., The ECM prediction is in accordance with the observed clinical DDI data (observed plasma AUCR of 1.14) that showed CYP3A4 inhibition did not alter pevonedistat exposure systemically, although ECM predicted liver AUCR of 2.85. Collectively, these data indicated that the hepatic uptake is the rate-determining step in the CLH of pevonedistat and are consistent with the lack of systemic clinical DDI with itraconazole. SIGNIFICANCE STATEMENT: In this study, we successfully demonstrated that the hepatic uptake is the rate-determining step in the CLH of pevonedistat. Both the conventional and extended clearance models predict CLplasma of pevonedistat well however, only the ECM accurately predicted DDI effect in the presence of itraconazole, thus providing further evidence for the lack of DDI with CYP3A4 perpetrators for drugs that involve complex transporter-enzyme interplay as there are currently not many examples in the literature except prototypical OATP substrate drugs.

Understanding Interindividual Variability in the Drug Interaction of a Highly Extracted CYP1A2 Substrate Tizanidine: Application of a Permeability-Limited Multicompartment Liver Model in a Population Based Physiologically Based Pharmacokinetic Framework

Tizanidine, a centrally acting skeletal muscle relaxant, is predominantly metabolized by CYP1A2 and undergoes extensive hepatic first-pass metabolism after oral administration. As a highly extracted drug, the systemic exposure to tizanidine exhibits considerable interindividual variability and is altered substantially when coadministered with CYP1A2 inhibitors or inducers. The aim of the current study was to compare the performance of a permeability-limited multicompartment liver (PerMCL) model, which operates as an approximation of the dispersion model, and the well stirred model (WSM) for predicting tizanidine drug-drug interactions (DDIs). Physiologically based pharmacokinetic models were developed for tizanidine, incorporating the PerMCL model and the WSM, respectively, to simulate the interaction of tizanidine with a range of CYP1A2 inhibitors and inducers. Whereas the WSM showed a tendency to underpredict the fold change of tizanidine area under the plasma concentration-time curve (AUC ratio) in the presence of perpetrators, the use of PerMCL model increased precision (absolute average-fold error: 1.32-1.42 versus 1.58) and decreased bias (average-fold error: 0.97-1.25 versus 0.63) for the predictions of mean AUC ratios as compared with the WSM. The PerMCL model captured the observed range of individual AUC ratios of tizanidine as well as the correlation between individual AUC ratios and CYP1A2 activities without interactions, whereas the WSM was not able to capture these. The results demonstrate the advantage of using the PerMCL model over the WSM in predicting the magnitude and interindividual variability of DDIs for a highly extracted sensitive substrate tizanidine. SIGNIFICANCE STATEMENT: This study demonstrates the advantages of the PerMCL model, which operates as an approximation of the dispersion model, in mitigating the tendency of the WSM to underpredict the magnitude and variability of DDIs of a highly extracted CYP1A2 substrate tizanidine when it is administered with CYP1A2 inhibitors or inducers. The physiologically based pharmacokinetic modeling approach described herein is valuable to the understanding of drug interactions of highly extracted substrates and the source of its interindividual variability.

Application of empirical scalars to enable early prediction of human hepatic clearance using IVIVE in drug discovery: an evaluation of 173 drugs

The utilization of in vitro data to predict drug pharmacokinetics (PK) in vivo has been a consistent practice in early drug discovery for decades. However, its success is hampered by mispredictions attributed to uncharacterized biological phenomena/experimental artifacts. Predicted drug clearance (CL) from experimental data (i.e. hepatocyte intrinsic clearance: CLint, fraction unbound in plasma: fu,p) is often systematically underpredicted using the well-stirred model (WSM). The objective of this study was to evaluate using empirical scalars in the WSM to correct for CL mispredictions. Drugs (N=28) were used to generate numerical scalars on CLint (α), and fu,p (β) to minimize the error (AAFE) for CL predictions. These scalars were validated using an additional dataset (N=28 drugs) and applied to a non-redundant AstraZeneca (AZ) dataset available in the literature (N=117 drugs) for a total of 173 compounds. CL predictions using the WSM were improved for most compounds using an α value of 3.66 (~64%<2-fold) compared to no scaling (~46%<2-fold). Similarly, using a β value of 0.55 or combination of α and β scalars (values of 1.74 and 0.66, respectively) resulted in a similar improvement in predictions (~64%<2-fold and ~65%<2-fold, respectively). For highly bound compounds (fu,p{less than or equal to}0.01), AAFE was substantially reduced across all scaling methods. Using the β scalar alone or a combination of α and β appeared optimal; and produce larger magnitude corrections for highly-bound compounds. Some drugs are still disproportionally mispredicted, however the improvements in prediction error and simplicity of applying these scalars suggests its utility for early-stage CL predictions. Significance Statement In early drug discovery, prediction of human clearance using in vitro experimental data plays an essential role in triaging compounds prior to in vivo studies. These predictions have been systematically underestimated. Here we introduce empirical scalars calibrated on the extent of plasma protein binding that appear to improve clearance prediction across multiple datasets. This approach can be used in early phases of drug discovery prior to the availability of pre-clinical data for early quantitative predictions of human clearance.

Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure

Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in animals provide a degree of confidence for extrapolation to humans. In addition, prediction of in vivo properties can be used to improve design during drug discovery, help select compounds with better properties, and reduce the number of in vivo experiments. In this study, we generated machine learning models able to predict rat in vivo PK parameters and concentration-time PK profiles based on the molecular chemical structure and either measured or predicted in vitro parameters. The models were trained on internal in vivo rat PK data for over 3000 diverse compounds from multiple projects and therapeutic areas, and the predicted endpoints include clearance and oral bioavailability. We compared the performance of various traditional machine learning algorithms and deep learning approaches, including graph convolutional neural networks. The best models for PK parameters achieved R² = 0.63 [root mean squared error (RMSE) = 0.26] for clearance and R² = 0.55 (RMSE = 0.46) for bioavailability. The models provide a fast and cost-efficient way to guide the design of molecules with optimal PK profiles, to enable the prediction of virtual compounds at the point of design, and to drive prioritization of compounds for in vivo assays.

A Pharmacokinetic and Metabolism Study of the TRPC6 Inhibitor SH045 in Mice by LC-MS/MS

TRPC6, the sixth member of the family of canonical transient receptor potential (TRP) channels, contributes to a variety of physiological processes and human pathologies. This study extends the knowledge on the newly developed TRPC6 blocker SH045 with respect to its main target organs beyond the description of plasma kinetics. According to the plasma concentration-time course in mice, SH045 is measurable up to 24 h after administration of 20 mg/kg BW (i.v.) and up to 6 h orally. The short plasma half-life and rather low oral bioavailability are contrasted by its reported high potency. Dosage limits were not worked out, but absence of safety concerns for 20 mg/kg BW supports further dose exploration. The disposition of SH045 is described. In particular, a high extravascular distribution, most prominent in lung, and a considerable renal elimination of SH045 were observed. SH045 is a substrate of CYP3A4 and CYP2A6. Hydroxylated and glucuronidated metabolites were identified under optimized LC-MS/MS conditions. The results guide a reasonable selection of dose and application route of SH045 for target-directed preclinical studies in vivo with one of the rare high potent and subtype-selective TRPC6 inhibitors available.

Consideration of Fractional Distribution Parameter fd in the Chen and Gross Method for Tissue-to-Plasma Partition Coefficients: Comparison of Several Methods

PURPOSE

The tissue-to-plasma partition coefficient (K_p) describes the extent of tissue distribution in physiologically-based pharmacokinetic (PBPK) models. Constant-rate infusion studies are common for experimental determination of the steady-state K_p,ss, while the tissue-plasma concentration ratio (C_T/C_p) in the terminal phase after intravenous doses is often utilized. The Chen and Gross (C&G) method converts a terminal slope C_T/C_p to K_p,ss based on assumptions of perfusion-limited distribution in tissue-plasma equilibration. However, considering blood flow (Q_T) and apparent tissue permeability (f_upPS_in) in the rate of tissue distribution, this report extends the C&G method by utilizing a fractional distribution parameter (f_d).

METHODS

Relevant PBPK equations for non-eliminating and eliminating organs along with lung and liver were derived for the conversion of C_T/C_p values to K_p,ss. The relationships were demonstrated in rats with measured C_T/C_p and K_p,ss values and the model-dependent f_d for 8 compounds with a range of permeability coefficients. Several methods of assessing K_p were compared.

RESULTS

Utilizing f_d in an extended C&G method, our estimations of K_p,ss from C_T/C_p were improved, particularly for lower permeability compounds. However, four in silico methods for estimating K_p performed poorly across tissues in comparison with measured K_p values. Mathematical relationships between K_p and K_p,ss that are generally applicable for eliminating organs with tissue permeability limitations necessitates inclusion of an extraction ratio (ER) and f_d.

CONCLUSION

Since many different types/sources of K_p are present in the literature and used in PBPK models, these perspectives and equations should provide better insights in measuring and interpreting K_p values in PBPK.

Towards the Elucidation of the Pharmacokinetics of Voriconazole: A Quantitative Characterization of Its Metabolism

The small-molecule drug voriconazole (VRC) shows a complex and not yet fully understood metabolism. Consequently, its in vivo pharmacokinetics are challenging to predict, leading to therapy failures or adverse events. Thus, a quantitative in vitro characterization of the metabolism and inhibition properties of VRC for human CYP enzymes was aimed for. The Michaelis-Menten kinetics of voriconazole N-oxide (NO) formation, the major circulating metabolite, by CYP2C19, CYP2C9 and CYP3A4, was determined in incubations of human recombinant CYP enzymes and liver and intestine microsomes. The contribution of the individual enzymes to NO formation was 63.1% CYP2C19, 13.4% CYP2C9 and 29.5% CYP3A4 as determined by specific CYP inhibition in microsomes and intersystem extrapolation factors. The type of inhibition and inhibitory potential of VRC, NO and hydroxyvoriconazole (OH-VRC), emerging to be formed independently of CYP enzymes, were evaluated by their effects on CYP marker reactions. Time-independent inhibition by VRC, NO and OH-VRC was observed on all three enzymes with NO being the weakest and VRC and OH-VRC being comparably strong inhibitors of CYP2C9 and CYP3A4. CYP2C19 was significantly inhibited by VRC only. Overall, the quantitative in vitro evaluations of the metabolism contributed to the elucidation of the pharmacokinetics of VRC and provided a basis for physiologically-based pharmacokinetic modeling and thus VRC treatment optimization.

In Defense of Current Concepts and Applications of Clearance in Drug Development and Therapeutics

Clearance is one of the most widely quoted and applied pharmacokinetic concepts in drug development and therapy. Its foundations and associated models of drug elimination are well embedded and accepted within the scientific community. Recently, however, the prevailing views that have held us in good stead for the past almost 50 years have been challenged with the argument that organ clearance should not be based on elimination rate, now defined by extraction across the liver divided by incoming or systemic concentration, as in current practice, but rather, by the mean concentration of drug within the blood in the organ, which is model-dependent. We argue that all needed parameters already exist, and that the proposed new approach to organ clearance is confusing and unnecessary. SIGNIFICANCE STATEMENT: Clearance concepts are widely applied in drug development and therapy. Historically, hepatic clearance has been defined as the ratio of rate of elimination divided by ingoing blood concentration. Recently, this approach has been challenged arguing that clearance should be referenced to blood concentration within the liver extrapolation (IVIVE). There is no need for additional, a feature that corresponds to intrinsic clearance of the chosen clearance model, a widely accepted parameter in physiologically based pharmacokinetic (PBPK) and in vitro to in vivo extrapolation (IVIVE). There is no need for additional, confusing clearance terms, which offer no material benefit.

Assessment of the Kochak-Benet Equation for Hepatic Clearance for the Parallel-Tube Model: Relevance of Classic Clearance Concepts in PK and PBPK

This report reviews concepts related to operation of the classic parallel-tube model (PTM) for hepatic disposition and examines two recent proposals of a newly derived equation to describe hepatic clearance (CL_H). It is demonstrated that the proposed equation is identical to a re-arrangement of an earlier relationship from Pang and Rowland and provides a means of calculation of intrinsic clearance (CL_int,PTM) rather than CL_H as posed. We further demonstrate how classic hepatic clearance models with an assumed CL_int, while subject to numerous limitations, remain highly useful and necessary in both traditional pharmacokinetics (PK) and physiologically based pharmacokinetic (PBPK) modeling.

Artificial Intelligence in Drug Safety and Metabolism

The use of artificial intelligence methods in drug safety began in the early 2000s with applications such as predicting bacterial mutagenicity and hERG inhibition. The field has been endlessly expanding ever since and the models have become more complex. These approaches are now integrated into molecule risk assessment processes along with in vitro and in vivo methods. Today, artificial intelligence can be used in every phase of drug discovery and development, from profiling chemical libraries in early discovery, to predicting off-target effects in the mid-discovery phase, to assessing potential mutagenic impurities in development and degradants as part of life cycle management. This chapter provides an overview of artificial intelligence in drug safety and describes its application throughout the entire discovery and development process.

Recent developments in in vitro and in vivo models for improved translation of preclinical pharmacokinetics and pharmacodynamics data

Improved pharmacokinetics/pharmacodynamics (PK/PD) prediction in the early stages of drug development is essential to inform lead optimization strategies and reduce attrition rates. Recently, there have been significant advancements in the development of new in vitro and in vivo strategies to better characterize pharmacokinetic properties and efficacy of drug leads. Herein, we review advances in experimental and mathematical models for clearance predictions, advancements in developing novel tools to capture slowly metabolized drugs, in vivo model developments to capture human etiology for supporting drug development, limitations and gaps in these efforts, and a perspective on the future in the field.

Prediction of Drug Clearance from Enzyme and Transporter Kinetics

Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.

Predicting human pharmacokinetics from preclinical data: absorption

Predicting the rate and extent of oral absorption of drugs in humans has been a challenging task for new drug researchers. This tutorial reviews in vitro and PBPK methods reported in the past decades that are widely applied to predicting oral absorption in humans. The physicochemical property and permeability (typically obtained using Caco-2 system) data is the first necessity to predict the extent of absorption from the gut lumen to the intestinal epithelium (F_a). Intrinsic clearance measured using the human microsome or hepatocytes is also needed to predict the gut (F_g) and hepatic (F_h) bioavailability. However, there are many issues with the correction of the inter-laboratory variability, hepatic cell membrane permeability, CYP3A4 dependency, etc. The bioavailability is finally calculated as F = F_a × F_g × F_h. Although the rate of absorption differs by micro-environments and locations in the intestine, it may be simply represented by k_a. The k_a, the first-order absorption rate constant, is predicted from in vitro and in vivo data. However, human PK-predicting software based on these PBPK theories should be carefully used because there are many assumptions and variances. They include differences in laboratory methods, inter-laboratory variances, and theories behind the methods. Thus, the user's knowledge and experiences in PBPK and in vitro methods are necessary for proper human PK prediction.

The Segregated Intestinal Flow Model (SFM) for Drug Absorption and Drug Metabolism: Implications on Intestinal and Liver Metabolism and Drug-Drug Interactions

The properties of the segregated flow model (SFM), which considers split intestinal flow patterns perfusing an active enterocyte region that houses enzymes and transporters (<20% of the total intestinal blood flow) and an inactive serosal region (>80%), were compared to those of the traditional model (TM), wherein 100% of the flow perfuses the non-segregated intestine tissue. The appropriateness of the SFM model is important in terms of drug absorption and intestinal and liver drug metabolism. Model behaviors were examined with respect to intestinally (M1) versus hepatically (M2) formed metabolites and the availabilities in the intestine (F_I) and liver (F_H) and the route of drug administration. The %contribution of the intestine to total first-pass metabolism bears a reciprocal relation to that for the liver, since the intestine, a gateway tissue, regulates the flow of substrate to the liver. The SFM predicts the highest and lowest M1 formed with oral (po) and intravenous (iv) dosing, respectively, whereas the extent of M1 formation is similar for the drug administered po or iv according to the TM, and these values sit intermediate those of the SFM. The SFM is significant, as this drug metabolism model explains route-dependent intestinal metabolism, describing a higher extent of intestinal metabolism with po versus the much reduced or absence of intestinal metabolism with iv dosing. A similar pattern exists for drug–drug interactions (DDIs). The inhibitor or inducer exerts its greatest effect on victim drugs when both inhibitor/inducer and drug are given po. With po dosing, more drug or inhibitor/inducer is brought into the intestine for DDIs. The bypass of flow and drug to the enterocyte region of the intestine after intravenous administration adds complications to in vitro–in vivo extrapolations (IVIVE).