Evolving approaches on measurements and applications of intracellular free drug concentration and Kpuu in drug discovery

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue

Artificial intelligence is expected to help identify excellent candidates in drug discovery. However, we face a lack of data, as it is time-consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel quantitative structure-activity relationship (QSAR) method to predict a parameter more precisely from an incomplete data set via optimizing data handling by making use of predicted explanatory variables. As a case study we focused on the tissue-to-plasma partition coefficient (Kp), which is an important parameter for understanding drug distribution in tissues and building the physiologically based pharmacokinetic model and is a representative of small and sparse data sets. In this study, we predicted the Kp values of 119 compounds in nine tissues (adipose, brain, gut, heart, kidney, liver, lung, muscle, and skin), although some of these were not available. To fill the missing values in Kp for each tissue, first we predicted those Kp values by the nonmissing data set using a random forest (RF) model with in vitro parameters (log P, fu, Drug Class, and fi) like a classical prediction by a QSAR model. Next, to predict the tissue-specific Kp values in a test data set, we constructed a second RF model with not only in vitro parameters but also the Kp values of other tissues (i.e., other than target tissues) predicted by the first RF model as explanatory variables. Furthermore, we tested all possible combinations of explanatory variables and selected the model with the highest predictability from the test data set as the final model. The evaluation of Kp prediction accuracy based on the root-mean-square error and R2 value revealed that the proposed models outperformed other machine learning methods such as the conventional RF and message-passing neural networks. Significant improvements were observed in the Kp values of adipose tissue, brain, kidney, liver, and skin. These improvements indicated that the Kp information on other tissues can be used to predict the same for a specific tissue. Additionally, we found a novel relationship between each tissue by evaluating all combinations of explanatory variables. In conclusion, we developed a novel RF model to predict Kp values. We hope that this method will be applied to various problems in the field of experimental biology which often contains missing values in the near future.

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.

Synthesis and Structure-Activity Relationships for Glutamate Transporter Allosteric Modulators

Excitatory amino acid transporters (EAATs) are essential CNS proteins that regulate glutamate levels. Excess glutamate release and alteration in EAAT expression are associated with several CNS disorders. Previously, we identified positive allosteric modulators (PAM) of EAAT2, the main CNS transporter, and have demonstrated their neuroprotective properties in vitro. Herein, we report on the structure-activity relationships (SAR) for the analogs identified from virtual screening and from our medicinal chemistry campaign. This work identified several selective EAAT2 positive allosteric modulators (PAMs) such as compounds 4 (DA-023) and 40 (NA-014) from a library of analogs inspired by GT949, an early generation compound. This series also provides nonselective EAAT PAMs, EAAT inhibitors, and inactive compounds that may be useful for elucidating the mechanism of EAAT allosteric modulation.

A Fab of trastuzumab to treat HER2 overexpressing breast cancer brain metastases

Despite major therapeutic advances for two decades, including the most recently approved anti-HER2 drugs, brain metastatic localizations remain the major cause of death for women with metastatic HER2 breast cancer. The main reason is the limited drug passage of the blood-brain barrier after intravenous injection and the significant efflux of drugs, including monoclocal antibodies, after administration into the cerebrospinal fluid. We hypothesized that this efflux was linked to the presence of a FcRn receptor in the blood-brain barrier. To overcome this efflux, we engineered two Fab fragments of trastuzumab, an anti-HER2 monoclonal antibody, and did a thorough preclinical development for therapeutic translational purpose. We demonstrated the safety and equal efficacy of the Fabs with trastuzumab in vitro, and in vivo using a patient-derived xenograft model of HER2 overexpressing breast cancer. For the pharmacokinetic studies of intra-cerebrospinal fluid administration, we implemented original rat models with catheter implanted into the cisterna magna. After intraventricular administration in rats, we demonstrated that the brain-to-blood efflux of Fab was up to 10 times lower than for trastuzumab, associated with a two-fold higher brain penetration compared to trastuzumab. This Fab, capable of significantly reducing brain-to-blood efflux and enhancing brain penetration after intra-cerebrospinal fluid injection, could thus be a new and original effective drug in the treatment of HER2 breast cancer brain metastases, which will be demonstrated by a phase I clinical trial dedicated to women in resort situations.

Comparison of Tumor Binding Across Tumor Types and Cell Lines to Support Free Drug Considerations for Oncology Drug Discovery

Tumor binding is an important parameter to derive unbound tumor concentration to explore pharmacokinetics (PK) and pharmacodynamics (PD) relationships for oncology disease targets. Tumor binding was evaluated using eleven matrices, including various commonly used ex vivo human and mouse xenograft and syngeneic tumors, tumor cell lines and liver as a surrogate tissue. The results showed that tumor binding is highly correlated among the different tumors and tumor cell lines except for the mouse melanoma (B16F10) tumor type. Liver fraction unbound (fu) has a good correlation with B16F10 tumor binding. Liver also demonstrates a two-fold equivalency, on average, with binding of other tumor types when a scaling factor is applied. Predictive models were developed for tumor binding, with correlations established with LogD (acids), predicted muscle fu (neutrals) and measured plasma protein binding (bases) to estimate tumor fu when experimental data are not available. Many approaches can be applied to obtain and estimate tumor binding values. One strategy proposed is to use a surrogate tumor tissue, such as mouse xenograft ovarian cancer (OVCAR3) tumor, as a surrogate for tumor binding (except for B16F10) to provide an early assessment of unbound tumor concentrations for development of PK/PD relationships.

A 20-Year Research Overview: Quantitative Prediction of Hepatic Clearance Using the In Vitro-In Vivo Extrapolation Approach Based on Physiologically Based Pharmacokinetic Modeling and Extended Clearance Concept

Understanding the extended clearance concept and establishing a physiologically based pharmacokinetic (PBPK) model are crucial for investigating the impact of changes in transporter and metabolizing enzyme abundance/functions on drug pharmacokinetics in blood and tissues. This mini-review provides an overview of the extended clearance concept and a PBPK model that includes transporter-mediated uptake processes in the liver. In general, complete in vitro and in vivo extrapolation (IVIVE) poses challenges due to missing factors that bridge the gap between in vitro and in vivo systems. By considering key in vitro parameters, we can capture in vivo pharmacokinetics, a strategy known as the top-down or middle-out approach. We present the latest progress, theory, and practice of the Cluster Gauss-Newton method, which is used for middle-out analyses. As examples of poor IVIVE, we discuss "albumin-mediated hepatic uptake" and "time-dependent inhibition" of OATP1Bs. The hepatic uptake of highly plasma-bound drugs is more efficient than what can be accounted for by their unbound concentration alone. This phenomenon is referred to as "albumin-mediated" hepatic uptake. IVIVE was improved by measuring hepatic uptake clearance in vitro in the presence of physiologic albumin concentrations. Lastly, we demonstrate the application of Cluster Gauss-Newton method-based analysis to the target-mediated drug disposition of bosentan. Incorporating saturable target binding and OATP1B-mediated hepatic uptake into the PBPK model enables the consideration of nonlinear kinetics across a wide dose range and the prediction of receptor occupancy over time. SIGNIFICANCE STATEMENT: There have been multiple instances where researchers' endeavors to unravel the underlying mechanism of poor in vitro-in vivo extrapolation have led to the discovery of previously undisclosed truths. These include 1) albumin-mediated hepatic uptake, 2) the target-mediated drug disposition in small molecules, and 3) the existence of a trans-inhibition mechanism by inhibitors for OATP1B-mediated hepatic uptake of drugs. Consequently, poor in vitro-in vivo extrapolation and the subsequent inquisitiveness of scientists may serve as a pivotal gateway to uncover hidden mechanisms.

Physiologically Based Pharmacokinetic Model of Brain Delivery of Plasma Protein Bound Drugs

INTRODUCTION

A physiologically based pharmacokinetic (PBPK) model is developed that focuses on the kinetic parameters of drug association and dissociation with albumin, alpha-1 acid glycoprotein (AGP), and brain tissue proteins, as well as drug permeability at the blood-brain barrier, drug metabolism, and brain blood flow.

GOAL

The model evaluates the extent to which plasma protein-mediated uptake (PMU) of drugs by brain influences the concentration of free drug both within the brain capillary compartment in vivo and the brain compartment. The model also studies the effect of drug binding to brain tissue proteins on the concentration of free drug in brain.

METHODS

The steady state and non-steady state PBPK models are comprised of 11-12 variables, and 18-23 parameters, respectively. Two model drugs are analyzed: propranolol, which undergoes modest PMU from the AGP-bound pool, and imipramine, which undergoes a high degree of PMU from both the albumin-bound and AGP-bound pools in plasma.

RESULTS

The free propranolol concentration in brain is under-estimated 2- to fourfold by in vitro measurements of free plasma propranolol, and the free imipramine concentration in brain is under-estimated by 18- to 31-fold by in vitro measurements of free imipramine in plasma. The free drug concentration in brain in vivo is independent of drug binding to brain tissue proteins.

CONCLUSIONS

In vitro measurement of free drug concentration in plasma under-estimates the free drug in brain in vivo if PMU in vivo from either the albumin and/or the AGP pools in plasma takes place at the BBB surface.

Unbound Brain-to-Plasma Partition Coefficient, Kp,uu,brain-a Game Changing Parameter for CNS Drug Discovery and Development

PURPOSE

More than 15 years have passed since the first description of the unbound brain-to-plasma partition coefficient (Kp,uu,brain) by Prof. Margareta Hammarlund-Udenaes, which was enabled by advancements in experimental methodologies including cerebral microdialysis. Since then, growing knowledge and data continue to support the notion that the unbound (free) concentration of a drug at the site of action, such as the brain, is the driving force for pharmacological responses. Towards this end, Kp,uu,brain is the key parameter to obtain unbound brain concentrations from unbound plasma concentrations.

METHODS

To understand the importance and impact of the Kp,uu,brain concept in contemporary drug discovery and development, a survey has been conducted amongst major pharmaceutical companies based in Europe and the USA. Here, we present the results from this survey which consisted of 47 questions addressing: 1) Background information of the companies, 2) Implementation, 3) Application areas, 4) Methodology, 5) Impact and 6) Future perspectives.

RESULTS AND CONCLUSIONS

From the responses, it is clear that the majority of the companies (93%) has established a common understanding across disciplines of the concept and utility of Kp,uu,brain as compared to other parameters related to brain exposure. Adoption of the Kp,uu,brain concept has been mainly driven by individual scientists advocating its application in the various companies rather than by a top-down approach. Remarkably, 79% of all responders describe the portfolio impact of Kp,uu,brain implementation in their companies as 'game-changing'. Although most companies (74%) consider the current toolbox for Kp,uu,brain assessment and its validation satisfactory for drug discovery and early development, areas of improvement and future research to better understand human brain pharmacokinetics/pharmacodynamics translation have been identified.

Free Drug Theory - No Longer Just a Hypothesis?

The Free Drug Hypothesis is a well-established concept within the scientific lexicon pervading many areas of Drug Discovery and Development, and yet it is poorly defined by virtue of many variations appearing in the literature. Clearly, unbound drug is in dynamic equilibrium with respect to absorption, distribution, metabolism, elimination, and indeed, interaction with the desired pharmacological target. Binding interactions be they specific (e.g. high affinity) or nonspecific (e.g. lower affinity/higher capacity) are governed by the same fundamental physicochemical tenets including Hill-Langmuir Isotherms, the Law of Mass Action and Drug Receptor Theory. With this in mind, it is time to recognise a more coherent version and consider it the Free Drug Theory and a hypothesis no longer. Today, we have the experimental and modelling capabilities, pharmacological knowledge, and an improved understanding of unbound drug distribution (e.g. Kp_uu) to raise the bar on our understanding and analysis of experimental data. The burden of proof should be to rule out mechanistic possibilities and/or experimental error before jumping to the conclusion that any observations contradict these fundamentals.

An update on the importance of plasma protein binding in drug discovery and development

Introduction: Plasma protein binding (PPB) remains a controversial topic in drug discovery and development. Fraction unbound (f_u) is a critical parameter that needs to be measured accurately, because it has significant impacts on the predictions of drug-drug interactions (DDI), estimations of therapeutic indices (TI), and developments of PK/PD relationships.  However, it is generally not advisable to change PPB through structural modifications, because PPB on its own has little relevance for in vivo efficacy.Areas covered: PPB fundamentals are discussed including the three main classes of drug binding proteins (i.e., albumin, alpha1-acid glycoprotein, and lipoproteins) and their physicochemical properties, in vivo half-life, and synthesis rate.  State-of-the-art methodologies for PPB are highlighted. Applications of PPB in drug discovery and development are presented.Expert opinion: PPB is an old topic in pharmacokinetics, but there are still many misconceptions. Improving the accuracy of PPB for highly bound compounds is an ongoing effort in the field with high priority. As the field continues to generate high quality data, the regulatory agencies will increase their confidence in our ability to accurately measure PPB of highly bound compounds, and experimental fu values below 0.01 will more likely be used for DDI predictions in the future.