Predictive models for oral drug absorption: from in silico methods to integrated dynamical models

In-Silico Modeling in Drug Metabolism and Interaction: Current Strategies of Lead Discovery

BACKGROUND

Drug metabolism is a complex mechanism of human body systems to detoxify foreign particles, chemicals, and drugs through bio alterations. It involves many biochemical reactions carried out by invivo enzyme systems present in the liver, kidney, intestine, lungs, and plasma. After drug administration, it crosses several biological membranes to reach into the target site for binding and produces the therapeutic response. After that, it may undergo detoxification and excretion to get rid of the biological systems. Most of the drugs and its metabolites are excreted through kidney via urination. Some drugs and their metabolites enter into intestinal mucosa and excrete through feces. Few of the drugs enter into hepatic circulation where they go into the intestinal tract. The drug leaves the liver via the bile duct and is excreted through feces. Therefore, the study of total methodology of drug biotransformation and interactions with various targets is costly.

METHODS

To minimize time and cost, in-silico algorithms have been utilized for lead-like drug discovery. Insilico modeling is the process where a computer model with a suitable algorithm is developed to perform a controlled experiment. It involves the combination of both in-vivo and in-vitro experimentation with virtual trials, eliminating the non-significant variables from a large number of variable parameters. Whereas, the major challenge for the experimenter is the selection and validation of the preferred model, as well as precise simulation in real physiological status.

RESULTS

The present review discussed the application of in-silico models to predict absorption, distribution, metabolism, and excretion (ADME) properties of drug molecules and also access the net rate of metabolism of a compound.

CONCLUSION

It helps with the identification of enzyme isoforms; which are likely to metabolize a compound, as well as the concentration dependence of metabolism and the identification of expected metabolites. In terms of drug-drug interactions (DDIs), models have been described for the inhibition of metabolism of one compound by another, and for the compound-dependent induction of drug-metabolizing enzymes.

Understanding the Oral Absorption of Irbesartan Using Biorelevant Dissolution Testing and PBPK Modeling

Poorly soluble weak bases form a significant proportion of the drugs available in the market thereby making it imperative to understand their absorption behavior. This work aims to mechanistically understand the oral absorption behavior for a weakly basic drug, Irbesartan (IRB), by investigating its pH dependent solubility, supersaturation, and precipitation behavior. Simulations performed using the equilibrium solubility could not accurately predict oral absorption. A multi-compartmental biorelevant dissolution testing model was used to evaluate dissolution in the stomach and duodenal compartment and mimic oral drug administration. This model exhibited sustained intestinal supersaturation (2-4-fold) even upon varying flow rates (4 mL/min, 7 mL/min, and mono-exponential transfer) from gastric to intestinal compartment. Simulation of oral absorption using GastroPlus™ and dissolution data collectively predicted plasma exposure with higher accuracy (% prediction error values within ± 15%), thereby indicating that multi-compartment dissolution testing enabled an improved prediction for oral pharmacokinetics of Irbesartan. Additionally, precipitates obtained in the intestinal compartment were characterized to determine the factors underlying intestinal supersaturation of Irbesartan. The solid form of these precipitates was amorphous with considerable particle size reduction. This indicated that following gastric transit, precipitate formation in the amorphous form coupled with an approximately 10 times particle size reduction could be potential factors leading to the generation and sustenance of intestinal drug supersaturation.

A multiscale absorption and transit model for oral drug delivery: Formulation and applications during fasting conditions

Most Food and Drug Administration (FDA)-approved drugs are administered orally, despite the complex process of oral drug absorption that is difficult to analyze experimentally. Oral bioavailability is dependent on the drug compound as well as the physiological and anatomical states of the user. Thus, computational models have emerged to mechanistically capture and predict the oral absorption process. The current models are generally 0D compartmental models and are limited by (a) simplified physiological characteristics of the gastrointestinal tract (GIT), (b) semiempirical/analytical dissolution profiles of the tested drugs, (c) incorrect absorption for some drug BCS classes (class IIa, for example), (d) GITs size variability among population, (e) incorrectly predicting the absorption of drugs that are GIT target specific, and (f) erroneous mixing in the domain. In this study, we have developed a multiscale absorption and transit (MAT) toolkit to simulate the dissolution, transport, absorption, distribution, metabolism, and elimination of orally administered drugs in the human GIT at multiple levels. MAT was constructed by integrating the spatially accurate first-principles driven high-fidelity drug transport, dissolution, and absorption model in the human stomach and GIT using our recently published quasi-3D (Q3D) framework. The process integrated the multilayer intestine physiologically based pharmacokinetics models with the whole-body compartmental models to predict the systemic pharmacokinetics of oral drugs. The computational results showed that this multiscale tool was able to match the experimental concentration results (individual and population) better than the traditional compartmental models. Ultimately, MAT will be developed into a commercial product to meet urgent demands from pharmaceutical and biomedical industries.

The Impact of Carboxylesterases in Drug Metabolism and Pharmacokinetics

BACKGROUND

Carboxylesterases (CES) play a critical role in catalyzing hydrolysis of esters, amides, carbamates and thioesters, as well as bioconverting prodrugs and soft drugs. The unique tissue distribution of CES enzymes provides great opportunities to design prodrugs or soft drugs for tissue targeting. Marked species differences in CES tissue distribution and catalytic activity are particularly challenging in human translation.

METHODS

Review and summarization of CES fundamentals and applications in drug discovery and development.

RESULTS

Human CES1 is one of the most highly expressed drug metabolizing enzymes in the liver, while human intestine only expresses CES2. CES enzymes have moderate to high inter-individual variability and exhibit low to no expression in the fetus, but increase substantially during the first few months of life. The CES genes are highly polymorphic and some CES genetic variants show significant influence on metabolism and clinical outcome of certain drugs. Monkeys appear to be more predictive of human pharmacokinetics for CES substrates than other species. Low risk of clinical drug-drug interaction is anticipated for CES, although they should not be overlooked, particularly interaction with alcohols. CES enzymes are moderately inducible through a number of transcription factors and can be repressed by inflammatory cytokines.

CONCLUSION

Although significant advances have been made in our understanding of CESs, in vitro - in vivo extrapolation of clearance is still in its infancy and further exploration is needed. In vitro and in vivo tools are continuously being developed to characterize CES substrates and inhibitors.

Predicting Drug Extraction in the Human Gut Wall: Assessing Contributions from Drug Metabolizing Enzymes and Transporter Proteins using Preclinical Models

Intestinal metabolism can limit oral bioavailability of drugs and increase the risk of drug interactions. It is therefore important to be able to predict and quantify it in drug discovery and early development. In recent years, a plethora of models-in vivo, in situ and in vitro-have been discussed in the literature. The primary objective of this review is to summarize the current knowledge in the quantitative prediction of gut-wall metabolism. As well as discussing the successes of current models for intestinal metabolism, the challenges in the establishment of good preclinical models are highlighted, including species differences in the isoforms; regional abundances and activities of drug metabolizing enzymes; the interplay of enzyme-transporter proteins; and lack of knowledge on enzyme abundances and availability of empirical scaling factors. Due to its broad specificity and high abundance in the intestine, CYP3A is the enzyme that is frequently implicated in human gut metabolism and is therefore the major focus of this review. A strategy to assess the impact of gut wall metabolism on oral bioavailability during drug discovery and early development phases is presented. Current gaps in the mechanistic understanding and the prediction of gut metabolism are highlighted, with suggestions on how they can be overcome in the future.

Mathematical Models to Explore Potential Effects of Supersaturation and Precipitation on Oral Bioavailability of Poorly Soluble Drugs

Poorly soluble drugs are increasingly formulated into supersaturating drug delivery systems which may precipitate during oral delivery. The link between in vitro drug concentration profiles and oral bioavailability is under intense investigation. The objective of the present work was to develop closed-form analytical solutions that relate in vitro concentration profiles to the amount of drug absorbed using several alternate assumptions and only six parameters. Three parameters define the key features of the in vitro drug concentration-time profile. An additional three parameters focus on physiological parameters. Absorption models were developed based on alternate assumptions; the drug concentration in the intestinal fluid: (1) peaks at the same time and concentration as in vitro, (2) peaks at the same time as in vitro, or (3) reaches the same peak concentration as in vitro. The three assumptions provide very different calculated values of bioavailability. Using Case 2 assumptions, bioavailability enhancement was found to be less than proportional to in silico examples of dissolution enhancement. Case 3 assumptions lead to bioavailability enhancements that are more than proportional to dissolution enhancements. Using Case 1 predicts drug absorption amounts that fall in between Case 2 and 3. The equations developed based on the alternate assumptions can be used to quickly evaluate the potential improvement in bioavailability due to intentional alteration of the in vitro drug concentration vs. time curve by reformulation. These equations may be useful in making decisions as to whether reformulation is expected to provide sufficient bioavailability enhancement to justify the effort.

Rationalizing underprediction of drug clearance from enzyme and transporter kinetic data: from in vitro tools to mechanistic modeling

Over the years, there has been an increase in the number and quality of available in vitro tools for the assessment of clearance. Complexity of data analysis and modelling of corresponding in vitro data has increased in an analogous manner, in particular for the simultaneous characterization of transporter and metabolism kinetics, together with intracellular binding and passive diffusion. In the current chapter, the impact of different factors on the in vitro-in vivo extrapolation of clearance will be addressed in a stepwise manner, from the selection of the most adequate in vitro system and experimental design/condition to the corresponding modelling of data generated. The application of static or physiologically based pharmacokinetic models in the prediction of clearance will be discussed, highlighting limitations and current challenges of some of the approaches. Particular focus will be on the ability of in vitro and in silico predictive tools to overcome the trend of clearance underprediction. Improvements made as a result of inclusion of extrahepatic metabolism and consideration of transporter-metabolism interplay across different organs will be discussed.

Paediatric oral biopharmaceutics: key considerations and current challenges

The complex process of oral drug absorption is influenced by a host of drug and formulation properties as well as their interaction with the gastrointestinal environment in terms of drug solubility, dissolution, permeability and pre-systemic metabolism. For adult dosage forms the use of biopharmaceutical tools to aid in the design and development of medicinal products is well documented. This review considers current literature evidence to guide development of bespoke paediatric biopharmaceutics tools and reviews current understanding surrounding extrapolation of adult methodology into a paediatric population. Clinical testing and the use of in silico models were also reviewed. The results demonstrate that further work is required to adequately characterise the paediatric gastrointestinal tract to ensure that biopharmaceutics tools are appropriate to predict performance within this population. The most vulnerable group was found to be neonates and infants up to 6 months where differences from adults were greatest.

Biorelevant media for transport experiments in the Caco-2 model to evaluate drug absorption in the fasted and the fed state and their usefulness

In this work we developed and characterized transport media that simulate the composition of micellar phase of intestinal fluids in the fasted and, especially, in the fed state and are appropriate for evaluating intestinal drug permeability characteristics using the Caco-2 model (FaSSIF-TM(Caco) and FeSSIF-TM(Caco), respectively). Media composition was based on FaSSIF-V2 and FeSSIF-V2 and recently reported data on total lipid concentrations in the micellar phase of contents of the upper small intestine in the fasted and the fed state and was adapted for cell culture compatibility. Permeation data were evaluated by compartmental kinetic modeling. Permeability coefficients, P, of hydrophilic drugs were not affected by media composition. In contrast, P values of a series of lipophilic compounds measured with FaSSIF-TM(Caco) and FeSSIF-TM(Caco), and reflecting transport by diffusion were smaller than those obtained with a purely aqueous reference transport medium, aq-TM(Caco), following the rank order aq-TM(Caco)>FaSSIF-TM(Caco)>FeSSIF-TM(Caco). The decline of permeability values was stronger as lipophilicity of the compounds increased. Compared with values estimated using aq-TM(Caco), permeability was reduced, depending on the compound, by more than 20- to 100-fold when measured with FeSSIF-TM(Caco) whereas compound ranking in regard to the permeability characteristics was also affected. The impact of reduced P value on flux through the mucosa, hence on drug absorption, in combination with the drug amount loaded on colloidal particles needs to be taken into consideration in PBPK modeling especially when the food effect is evaluated.

PBPK models for the prediction of in vivo performance of oral dosage forms

Drug absorption from the gastrointestinal (GI) tract is a highly complex process dependent upon numerous factors including the physicochemical properties of the drug, characteristics of the formulation and interplay with the underlying physiological properties of the GI tract. The ability to accurately predict oral drug absorption during drug product development is becoming more relevant given the current challenges facing the pharmaceutical industry. Physiologically-based pharmacokinetic (PBPK) modeling provides an approach that enables the plasma concentration-time profiles to be predicted from preclinical in vitro and in vivo data and can thus provide a valuable resource to support decisions at various stages of the drug development process. Whilst there have been quite a few successes with PBPK models identifying key issues in the development of new drugs in vivo, there are still many aspects that need to be addressed in order to maximize the utility of the PBPK models to predict drug absorption, including improving our understanding of conditions in the lower small intestine and colon, taking the influence of disease on GI physiology into account and further exploring the reasons behind population variability. Importantly, there is also a need to create more appropriate in vitro models for testing dosage form performance and to streamline data input from these into the PBPK models. As part of the Oral Biopharmaceutical Tools (OrBiTo) project, this review provides a summary of the current status of PBPK models available. The current challenges in PBPK set-ups for oral drug absorption including the composition of GI luminal contents, transit and hydrodynamics, permeability and intestinal wall metabolism are discussed in detail. Further, the challenges regarding the appropriate integration of results from in vitro models, such as consideration of appropriate integration/estimation of solubility and the complexity of the in vitro release and precipitation data, are also highlighted as important steps to advancing the application of PBPK models in drug development. It is expected that the "innovative" integration of in vitro data from more appropriate in vitro models and the enhancement of the GI physiology component of PBPK models, arising from the OrBiTo project, will lead to a significant enhancement in the ability of PBPK models to successfully predict oral drug absorption and advance their role in preclinical and clinical development, as well as for regulatory applications.

Integrating drug permeability with dissolution profile to develop IVIVC

In this review article, three different approaches to predict in vivo oral absorption based on the in vitro data of drug permeability, solubility and dissolution were introduced. At the drug discovery stage, the absorption potential of each candidate is most important to select better compounds for further development. The concept of maximum absorbable dose is applied widely, not only to evaluate the absorption potential but also to elucidate the rate-limiting process of oral absorption that helps us to understand the cause of poor absorption. To integrate the permeability of the drug with its dissolution profile, two different approaches, in vitro dissolution/permeation system (D/P system) and in silico model and simulation method, are proposed. In the D/P system, by mimicking the in vivo process of drug absorption, the permeated amount of drugs, that is the total output of dissolution and permeation processes, are correlated with the fraction absorbed in human (F(a)). This system is powerful for evaluating the improved absorption by various formulations and the effect of food intake. On the other hand, in the model and simulation approach, an intrinsic dissolution parameter of drug particle, z, was extracted from the small scale in vitro test and the process of intestinal absorption was re-constructed in silico by incorporating the physiological parameters in human. The effective use of these approaches for the development of oral drug products is discussed through various case studies.

Physiologically-based pharmacokinetic (PBPK) models in toxicity testing and risk assessment

Physiologically-based pharmacokinetic (PBPK) modeling offers a scientifically-sound framework for integrating mechanistic data on absorption, distribution, metabolism and elimination to predict the time-course of parent chemical, metabolite(s) or biomarkers in the exposed organism. A major advantage of PBPK models is their ability to forecast the impact of specific mechanistic processes and determinants on the tissue dose. In this regard, they facilitate integration of data obtained with in vitro and in silico methods, for making predictions of the tissue dosimetry in the whole animal, thus reducing and/or refining the use of animals in pharmacokinetic and toxicity studies. This chapter presents the principles and practice of PBPK modeling, as well as the application of these models in toxicity testing and health risk assessments.

Applications of human pharmacokinetic prediction in first-in-human dose estimation

Quantitative estimations of first-in-human (FIH) doses are critical for phase I clinical trials in drug development. Human pharmacokinetic (PK) prediction methods have been developed to project the human clearance (CL) and bioavailability with reasonable accuracy, which facilitates estimation of a safe yet efficacious FIH dose. However, the FIH dose estimation is still very challenging and complex. The aim of this article is to review the common approaches for FIH dose estimation with an emphasis on PK-guided estimation. We discuss 5 methods for FIH dose estimation, 17 approaches for the prediction of human CL, 6 methods for the prediction of bioavailability, and 3 tools for the prediction of PK profiles. This review may serve as a practical protocol for PK- or pharmacokinetic/pharmacodynamic-guided estimation of the FIH dose.

A pH-dilution method for estimation of biorelevant drug solubility along the gastrointestinal tract: application to physiologically based pharmacokinetic modeling

Physiologically based pharmacokinetic (PBPK) modeling tools have become an integral part of the modern drug discovery-development process. However, accurate PK prediction of enabling formulations of poorly soluble compounds by applying PBPK modeling has been very limited. This is because current PBPK models rely only on thermodynamic drug solubility inputs (e.g., pH-solubility profile) and give little consideration to the dynamic changes in apparent drug solubility (e.g., supersaturation) that occur during gastrointestinal (GI) transit of an enabling formulation of a water insoluble drug. Moreover, biorepresentative and predictive in vitro tools to measure formulation dependent solubility changes during GI transit remain underdeveloped. In this work, we have developed an in vitro dual pH-dilution method based on rat physiology to estimate the apparent drug concentration in solution along the GI tract during release from solubility enabling formulations. This simple dual pH-dilution method was evaluated using various solubility enabling formulations (i.e., cosolvent solution, amorphous solid dispersions) made using a model early development drug candidate with poor aqueous solubility. The in vitro drug concentration-time profiles from the enabling formulations were used as solubility inputs for PBPK modeling using GastroPlus software. This resulted in excellent predictions of the in vivo oral plasma concentration-time profiles, as compared to using the traditional inputs of thermodynamic pH-solubility profiles. In summary, this work describes a novel in vitro method for facile estimation of formulation dependent GI drug concentration-time profiles and demonstrates the utility of PBPK modeling for oral PK prediction of enabling formulations of poorly soluble drugs.

Mechanistic approaches to predicting oral drug absorption

Modeling and simulation of oral drug absorption have been widely used in drug discovery, development, and regulation. Predictive absorption models are used to determine the rate and extent of oral drug absorption, facilitate lead drug candidate selection, establish formulation development strategy, and support the development of regulatory policies. This review highlights the development of recent drug absorption models including dispersion and compartmental models. The compartmental models include the compartmental absorption and transit model; Grass model; gastrointestinal transit absorption model; advanced compartmental absorption and transit model; and advanced dissolution, absorption, and metabolism model. Compared to the early absorption models, the above models developed or extended since the mid-1990s have demonstrated greatly improved predictive performance by accounting for multiple factors such as drug degradation, gastric emptying, intestinal transit, first-pass metabolism, and intestinal transport. For future model development, more heterogeneous features of the gastrointestinal tract (villous blood flow, metabolizing enzymes, and transporters), food effects, and drug-drug interactions should be fully characterized and taken into consideration. Moreover, predicting population inter- and intravariability in oral drug absorption can be useful and important for the evaluation of clinical safety and efficacy of drugs. Establishing databases and libraries that contain accurate pharmaceutical and pharmacokinetic information for commercialized and uncommercialized drugs may also be helpful for model development and validation.

Potential role of intestinal first-pass metabolism in the prediction of drug-drug interactions

BACKGROUND

The contribution of intestine to the magnitude of drug-drug interactions (DDI) may be significant, considering high levels of inhibitors in the gut lumen achieved during absorption and the abundance of metabolic enzymes in the mature enterocytes. Intestinal inhibition is incorporated in the DDI prediction models as the ratio of the intestinal wall availability in the presence and absence of the inhibitor (F(G)(') and F(G), respectively).

OBJECTIVE

This review will focus on the ability of the current approaches to estimate the extent of intestinal DDI accurately, addressing predominantly the most abundant intestinal P450 enzyme, CYP3A4.

METHODS

Considering the sensitivity of the DDI prediction models to the accuracy of the F(G) estimates, the current study focuses on 3 different in vitro and in vivo approaches to assess this parameter.

RESULTS/CONCLUSION

The advantages and limitations of each of F(G) methods are outlined. Accurate assessment of this parameter is essential for the prediction of human drug clearance and drug-drug interaction potential.