Studying the right transporter at the right time: an in vitro strategy for assessing drug-drug interaction risk during drug discovery and development.

INTRODUCTION: Transporters are significant in dictating drug pharmacokinetics, thus inhibition of transporter function can alter drug concentrations resulting in drug-drug interactions (DDIs). Because they can impact drug toxicity, transporter DDIs are a regulatory concern for which prediction of clinical effect from in vitro data is critical to understanding risk. AREA COVERED: The authors propose in vitro strategies to assist mitigating/removing transporter DDI risk during development by frontloading specific studies, or managing patient risk in the clinic. An overview of clinically relevant drug transporters and observed DDIs is provided, alongside presentation of key considerations/recommendations for in vitro study design evaluating drugs as inhibitors or substrates. Guidance on identifying critical co-medications, clinically relevant disposition pathways, and using mechanistic static equations for quantitative prediction of DDI is compiled. EXPERT OPINION: The strategies provided will facilitate project teams to study the right transporter at the right time to minimize development risks associated with DDIs. To truly alleviate or manage clinical risk, the industry will benefit from moving away from current qualitative basic static equation approaches to transporter DDI hazard assessment towards adopting the use of mechanistic models to enable quantitative DDI prediction, thereby contextualizing risk to ascertain whether a transporter DDI is simply pharmacokinetic or clinically significant requiring intervention.

The evolution of strategies to minimise the risk of human drug-induced liver injury (DILI) in drug discovery and development.

Early identification of toxicity associated with new chemical entities (NCEs) is critical in preventing late-stage drug development attrition. Liver injury remains a leading cause of drug failures in clinical trials and post-approval withdrawals reflecting the poor translation between traditional preclinical animal models and human clinical outcomes. For this reason, preclinical strategies have evolved over recent years to incorporate more sophisticated human in vitro cell-based models with multi-parametric endpoints. This review aims to highlight the evolution of the strategies adopted to improve human hepatotoxicity prediction in drug discovery and compares/contrasts these with recent activities in our lab. The key role of human exposure and hepatic drug uptake transporters (e.g. OATPs, OAT2) is also elaborated.

Estimating human ADME properties, pharmacokinetic parameters and likely clinical dose in drug discovery.

Introduction: Prediction of human absorption, distribution, metabolism, and excretion (ADME) properties, therapeutic dose and exposure has become an integral part of compound optimization in discovery. Incorporation of drug metabolism and pharmacokinetics into discovery projects has largely tempered historical drug failure due to sub-optimal ADME. In the current era, inadequate safety and efficacy are leading culprits for attrition; both of which are dependent upon drug exposure. Therefore, prediction of human pharmacokinetics (PK) and dose are core components of de-risking strategies in discovery. Areas covered: The authors provide an overview of human dose prediction methods and present a toolbox of PK parameter prediction models with a proposed framework for a consensus approach valid throughout the discovery value chain. Mechanistic considerations and indicators for their application are discussed which may impact the dose prediction approach. Examples are provided to illustrate how implementation of the proposed strategy throughout discovery can assist project progression. Expert opinion: Anticipation of human ADME, therapeutic dose and exposure must be deliberated throughout drug discovery from virtual/initial synthesis where key properties are considered and similar molecules ranked, into development where advanced compounds can be subject to prediction with greater mechanistic understanding and data-driven model selection.

Mechanistic In Vitro Studies Indicate that the Clinical Drug-Drug Interaction between Telithromycin and Simvastatin Acid Is Driven by Time-Dependent Inhibition of CYP3A4 with Minimal Effect on OATP1B1.

A previous attempt to accurately quantify the increased simvastatin acid exposure due to drug-drug interaction (DDI) with coadministered telithromycin, using a mechanistic static model, substantially underpredicted the magnitude of the area under the plasma concentration-time curve ratio (AUCR) based on reversible inhibition of CYP3A4 and organic anion transporting polypeptide 1B1 (OATP1B1). To reconcile this disconnect between predicted and clinically observed AUCR, telithromycin was evaluated as a time-dependent inhibitor of CYP3A4 in vitro, as well as an inhibitor of OATP1B1. Telithromycin inhibited OATP1B1-mediated [3H]-estradiol 17ß-d-glucuronide (0.02 µM) transport with a mean IC50 of 12.0 ± 1.45 µM and was determined by IC50 shift and kinetic analyses to be a competitive reversible inhibitor of CYP3A4-mediated midazolam1- hydroxylation with a mean absolute inhibition constant (Ki) value of 3.65 ± 0.531 µM. The 2.83-fold shift in IC50 (10.4-3.68 µM) after a 30-minute metabolic preincubation confirmed telithromycin as a time-dependent inhibitor of CYP3A4; the mean inhibitor concentration that causes half-maximal inactivation of enzyme (KI) and maximal rate of inactivation of enzyme (kinact) values determined for inactivation were 1.05 ± 0.226 µM and 0.02772 ± 0.00272 min-1, respectively. After the integration of an enzyme time-dependent inhibition component into the previous mechanistic static model using the in vitro inhibitory kinetic parameters determined above, the newly predicted simvastatin acid AUCR (10.8 or 5.4) resulting from perturbation of its critical disposition pathways matched the clinically observed AUCR (10.8 or 4.3) after coadministration, or staggered administration, with telithromycin, respectively. These results indicate the time-dependent inhibition of CYP3A4 by telithromycin as the primary driver underlying its clinical DDI with simvastatin acid.

Development and Characterization of a Human Hepatocyte Low Intrinsic Clearance Assay for Use in Drug Discovery.

Progression of new chemical entities is a multiparametric process involving a balance of potency; absorption, distribution, metabolism, and excretion; and safety properties. To accurately predict human pharmacokinetics and estimate human efficacious dose, the use of in vitro measures of clearance is often essential. Low metabolic clearance is often targeted to facilitate in vivo exposure and achieve appropriate half-life. Suspension primary human hepatocytes (PHHs) have been successfully used in predictions of clearance. However, incubation times are limited, hindering the limit of quantification. The aims herein were to evaluate the ability of a novel PHH media supplement, HepExtend, in order to maintain cell function, increase culture times, and define the clearance of stable compounds. Cell activity was analyzed with a range of cytochrome P450 (P450) and UDP-glucuronosyltransferase (UGT) substrates, and the mRNA expression of drug disposition and toxicity marker genes was determined. HepExtend and Geltrex were essential to maintain cell activity and viability for 5 days (N = 3 donors). In comparison with CM4000 ± Geltrex, HepExtend + Geltrex displayed a higher level of gene expression on day 1, particularly for the P450s, nuclear receptors, and UGTs. The novel medium, HepExtend + Geltrex, was robust and reproducible in generating statistically significant intrinsic clearance values at 0.1 µl/min/106 cells over a 30-hour period (P < 0.05), which was lower than previously demonstrated. Following regression correction, human hepatic in vivo clearance was predicted within 3-fold for 83% of compounds tested for three human donors, with an average fold error of 2.2. The novel PHH medium, HepExtend, with matrix overlay offers significant improvement in determining compounds with low intrinsic clearance when compared with alternative approaches.

Hepatic transporter drug-drug interactions: an evaluation of approaches and methodologies.

INTRODUCTION: Drug-drug interactions (DDIs) continue to account for 5% of hospital admissions and therefore remain a major regulatory concern. Effective, quantitative prediction of DDIs will reduce unexpected clinical findings and encourage projects to frontload DDI investigations rather than concentrating on risk management ('manage the baggage') later in drug development. A key challenge in DDI prediction is the discrepancies between reported models. Areas covered: The current synopsis focuses on four recent influential publications on hepatic drug transporter DDIs using static models that tackle interactions with individual transporters and in combination with other drug transporters and metabolising enzymes. These models vary in their assumptions (including input parameters), transparency, reproducibility and complexity. In this review, these facets are compared and contrasted with recommendations made as to their application. Expert opinion: Over the past decade, static models have evolved from simple [I]/ki models to incorporate victim and perpetrator disposition mechanisms including the absorption rate constant, the fraction of the drug metabolised/eliminated and/or clearance concepts. Nonetheless, models that comprise additional parameters and complexity do not necessarily out-perform simpler models with fewer inputs. Further, consideration of the property space to exploit some drug target classes has also highlighted the fine balance required between frontloading and back-loading studies to design out or 'manage the baggage'.

Harmonised high throughput microsomal stability assay.

INTRODUCTION: Prediction of human pharmacokinetics from in vitro assays and pre-clinical data is an integral part of drug discovery. In vitro stability metabolic studies can provide an estimate of in vivo hepatic intrinsic clearance through inclusion of biological scaling factors. Many labs have personalised stability protocols including marker compounds and have adopted QC criteria and assay limits to ensure data integrity. Contract research organisations (CRO's) provide integrated drug discovery support to academic and pharmaceutical/biotechnology institutions to progress their in-house projects. The majority of these clients have established in-house protocols with associated criteria to ensure data consistency between in-house and external labs. METHODS: In this study, numerous assay variables were condensed into one harmonised assay format and a range of compounds with diverse physicochemical properties were evaluated. The protocols were diverse with respect to the following attributes: buffer, microsomal concentration and species strain. RESULTS: Comparison of human lots in vitro CLint between the traditional and consolidated assay formats showed a good correlation with no significant difference. A clear relationship was demonstrated between strains. Interpretation of in vitro intrinsic clearance between the strains for each species was consistent. Using strict classes of in vitro hepatic intrinsic clearance values (<50, 50-100, >150µL/min/mg protein) comparisons across different conditions such as, assay variables, human lots, mouse and rat strains showed >80% agreement. DISCUSSION: A high throughput assay was developed that enables the simultaneous measurement of CLint using mouse, rat and human hepatic microsomes (consolidated assay). By condensing all possible variables into one assay format, consistent data were obtained during head to head tests.

Evaluation of a novel PXR-knockout in HepaRG™ cells.

The nuclear pregnane X receptor (PXR) regulates the expression of genes involved in the metabolism, hepatobiliary disposition, and toxicity of drugs and endogenous compounds. PXR is a promiscuous nuclear hormone receptor (NHR) with significant ligand and DNA-binding crosstalk with the constitutive androstane receptor (CAR); hence, defining the precise role of PXR in gene regulation is challenging. Here, utilising a novel PXR-knockout (KO) HepaRG cell line, real-time PCR analysis was conducted to determine PXR involvement for a range of inducers. The selective PXR agonist rifampicin, a selective CAR activator, 6-(4-chlorophenyl)imidazo[2,1-b][1,3]thiazole-5-carbaldehyde O-(3,4-dichlorobenzyl)oxime (CITCO), and dual activators of CAR and PXR including phenobarbital (PB) were analyzed. HepaRG control cells (5F clone) were responsive to prototypical inducers of CYP2B6 and CYP3A4. No response was observed in the PXR-KO cells treated with rifampicin. Induction of CYP3A4 by PB, artemisinin, and phenytoin was also much reduced in PXR-KO cells, while the response to CITCO was maintained. This finding is in agreement with the abolition of functional PXR expression. The apparent EC50 values for PB were in agreement between the cell lines; however, CITCO was ~threefold (0.3 µmol/L vs. 1 µmol/L) lower in the PXR-KO cells compared with the 5F cells for CYP2B6 induction. Results presented support the application of the novel PXR-KO cells in the definitive assignment of PXR-mediated CYP2B6 and CYP3A4 induction. Utilization of such cell lines will allow advancement in composing structure activity relationships rather than relying predominantly on pharmacological manipulations and provide in-depth mechanistic evaluation.

A new paradigm for navigating compound property related drug attrition.

Improving the efficiency of drug discovery remains a major focus for the pharmaceutical industry. Toxicity accounts for 90% of withdrawals and major early-stage terminations relate to suboptimal efficacy and safety. Traditional oral drug space is well defined with respect to physicochemical properties and ADMET risks but increased focus on ligand-lipophilicity efficiency, maximizing enthalpy contributions and new target classes challenge this paradigm. A hybrid space has been identified that combines physical properties and key interactions attributable to drug transporters. A novel algorithm is proposed that incorporates drug-transporter interactions and its utility evaluated against popular ligand efficiency indices. Simply reducing the bulk properties of compounds can exchange one problem for another and creates high-risk areas that challenge the successful delivery from a balanced portfolio.

Cytochrome P450 time-dependent inhibition and induction: advances in assays, risk analysis and modelling.

INTRODUCTION: It is widely accepted that current practice of polypharmacy inevitably increases the incidence of drug-drug interactions (DDIs). Serious DDIs are a major liability for new molecular entities entering the pharmaceutical market. Various strategies are employed to avoid problematic compounds for clinical development. Progress made with reversible CYP DDIs has prompted a switch to study and model time-dependent inhibition and induction interactions. AREAS COVERED: An overview of popular experimental practices is presented with discussion of techniques and algorithms used to analyse the clinical DDI risk. Emphasis is placed on the transition from early, simple static equations, via more complex net mechanistic, static models to dynamic approaches involving multiple perpetrators and metabolites, simultaneous inhibition and induction. EXPERT OPINION: Inclusion of the more conservative terms for parameters required for DDI evaluation may eliminate promising chemical space, encourages poor practice and hampers innovation. Breakthroughs have originated from understanding of 'outliers' from such analyses where CYP enzyme-transporter interplay may be involved. The role of key transporters in drug disposition is firmly established as the chemistry required to address new targets deviates from traditional 'drug-like' space. Attempts to model more complex interactions for substrates of both CYP enzymes and drug transporters are still in their infancy and will benefit from dynamic modelling.

Application of an in vitro OAT assay in drug design and optimization of renal clearance.

1. Optimization of renal clearance is a complex balance between passive and active processes mediated by renal transporters. This work aimed to characterize the interaction of a series of compounds with rat and human organic anion transporters (OATs) and develop quantitative structure-activity relationships (QSARs) to optimize renal clearance. 2. In vitro inhibition assays were established for human OAT1 and rat Oat3 and rat in vivo renal clearance was obtained. Statistically significant quantitative relationships were explored between the compounds' physical properties, their affinity for OAT1 and oat3 and the inter-relationship with unbound renal clearance (URC) in rat. 3. Many of the compounds were actively secreted and in vitro analysis demonstrated that these were ligands for rat and human OAT transporters (IC50 values ranging from <1 to >100 µM). Application of resultant QSAR models reduced renal clearance in the rat from 24 to <0.1 ml/min/kg. Data analysis indicated that the properties associated with increasing affinity at OATs are the same as those associated with reducing URC but orthogonal in nature. 4. This study has demonstrated that OAT inhibition data and QSAR models can be successfully used to optimize rat renal clearance in vivo and provide confidence of translation to humans.

Scaffold-hopping with zwitterionic CCR3 antagonists: identification and optimisation of a series with good potency and pharmacokinetics leading to the discovery of AZ12436092.

The discovery and optimisation of a series of zwitterionic CCR3 antagonists is described. Optimisation of the structure led to AZ12436092, a compound with excellent selectivity over activity at hERG and outstanding pharmacokinetics in preclinical species.

The discovery of CCR3/H1 dual antagonists with reduced hERG risk.

A series of dual CCR3/H(1) antagonists based on a bispiperidine scaffold were discovered. Introduction of an acidic group overcame hERG liability. Bioavailability was optimised by modulation of physico-chemical properties and physical form to deliver a compound suitable for clinical evaluation.

The development, characterization, and application of an OATP1B1 inhibition assay in drug discovery.

The pivotal role of organic anion-transporting polypeptide 1B1 (OATP1B1) in drug disposition has become clear over the last decade. Therefore, an OATP1B1 inhibition assay suitable for use within early drug discovery was developed and characterized. IC(50) estimates for 10 literature compounds using pitavastatin and estradiol-17ß-glucuronide as substrates were within 2-fold of each other. In addition, the IC(50) estimates using pitavastatin uptake agreed well with literature values (r(2) = 0.92, average fold error = 1.3). However, when estrone-3-sulfate was used, OATP1B1 inhibition was underpredicted by as much as 10-fold. A comparison of uptake in human hepatocytes and OATP1B1 inhibition showed a significant correlation (r(2) = 0.53, P < 0.001) for more than 40 compounds. These data suggest that, for discrete chemical series, OATP1B1 inhibition data may be used as a surrogate for more costly and time-consuming uptake studies in hepatocytes. OATP1B1 inhibition data, determined for over 260 compounds representing both internal AstraZeneca and literature chemistry, were also used to generate a continuous in silico model. The robustness of the model was demonstrated by accurately predicting OATP1B1 inhibition for external test sets using 50 AstraZeneca compounds (root mean square error = 0.45) and 12 literature drugs (RMSE = 0.32). The most important molecular descriptors for the prediction of OATP1B1 inhibition were maximal hydrogen bonding strength followed by cLogP. This study has shown that a well validated OATP1B1 inhibition assay in conjunction with an in silico approaches has the potential to influence significantly the design-make-test cycle and subsequently reduce the propensity of OATP1B1 ligands.

Prediction of human renal clearance from preclinical species for a diverse set of drugs that exhibit both active secretion and net reabsorption.

Identifying any extrahepatic excretion phenomenon in preclinical species is crucial for an accurate prediction of the pharmacokinetics in man. This understanding is particularly key for drugs with a small volume of distribution, because they require an especially low total clearance to be suitable for a once-a-day dosing regimen in man. In this study, three animal scaling techniques were applied for the prediction of the human renal clearance of 36 diverse drugs that show active secretion or net reabsorption: 1) direct correlations between renal clearance in man and each of the two main preclinical species (rat and dog); 2) simple allometry; and 3) Mahmood's renal clearance scaling method. The results show clearly that the predictions to man for the methods are improved significantly when corrections are made for species differences in plasma protein binding. Overall, the most accurate predictions were obtained by using a direct correlation with the dog renal clearance after correcting for differences in plasma protein binding and kidney blood flow (r² = 0.84), where predictions, on average, were within 2-fold of the observed renal clearance values in human.

The use of HepaRG and human hepatocyte data in predicting CYP induction drug-drug interactions via static equation and dynamic mechanistic modelling approaches.

The method of predicting CYP induction drug-drug interactions (DDIs) from a relative induction score (RIS) calibration has been developed to provide a novel model facilitating predictions for any CYP-inducer substrate combination by inclusion of parameters such as the fraction of hepatic clearance mediated by a specific CYP and fraction of the dose escaping intestinal extraction. In vitro HepaRG CYP3A4 induction data were used as a basis for the approach and a large number of DDIs were well predicted. Primary human hepatocyte data were also used to make predictions, using the HepaRG calibration as a foundation. Similar predictive accuracy suggests that HepaRG and primary hepatocyte data can be used inter-changeably within the same laboratory. A comparison of this 'indirect' calibration method with a direct in vitro-in vivo scaling approach was made and investigations undertaken to define the most appropriate in vivo inducer concentration to use. Additionally, a reasonably effective prediction model based on F(2) (the concentration of inducer taken to increase the CYP mRNA 2-fold above background) was established. An accurate prediction for the CYP1A2-dependent omeprazole-caffeine interaction was also made, demonstrating that the methods are useful for the evaluation of DDIs from induction involving mechanisms other than PXR activation. Finally, a dynamic mechanistic model accounting for the simultaneous influence of CYP induction and reversible and irreversible CYP inhibition in both the liver and intestine was written to provide a prediction of the overall DDI when several interactions occur concurrently. The rationale for using the various models described, alongside commercially available prediction tools, at various stages of the drug discovery process is described.

Impact of hepatic uptake transporters on pharmacokinetics and drug-drug interactions: use of assays and models for decision making in the pharmaceutical industry.

The ability to predict hepatic metabolic clearance is a key component in the design and selection of small molecule drug candidates within the pharmaceutical industry. The recognition that metabolism-transporter interplay can influence hepatic metabolic clearance has presented new challenges, both in terms of the creation of experimental systems suitable for an industry setting and also in developing an understanding of the pharmacokinetic concepts that underpin them. This paper reviews the pharmacokinetic principles that govern the kinetics of uptake transporter substrates. In addition, new data are presented from a range of test systems for assessing hepatic drug clearance and the impact of drug-drug interactions (DDIs).

Evaluation of multiple in vitro systems for assessment of CYP3A4 induction in drug discovery: human hepatocytes, pregnane X receptor reporter gene, and Fa2N-4 and HepaRG cells.

Prototypic CYP3A4 inducers were tested in a pregnane X receptor (PXR) reporter gene assay, Fa2N-4 cells, HepaRG cells, and primary human hepatocytes, along with negative controls, using CYP3A4 mRNA and activity endpoints, where appropriate. Over half of the compounds tested (14 of 24) were identified as time-dependent inhibitors of CYP3A4 and high mRNA/activity ratios (>10) were consistent with CYP3A4 time-dependent inhibition for compounds such as troleandomycin, ritonavir, and verapamil. Induction response was compared between two human donors; there was an excellent correlation in the EC(50) estimates (r(2) = 0.89, p < 0.001), and a weak but statistically significant correlation was noted for maximum observed induction at an optimum concentration (E(max)) (r(2) = 0.38, p = 0.001). E(max) and EC(50) estimates determined from the PXR reporter gene assay and Fa2N-4 and HepaRG cells were compared with those from hepatocytes. Overall, EC(50) values generated using hepatocytes agreed with those generated in the PXR reporter gene assay (r(2) = 0.85, p < 0.001) and Fa2N-4 (r(2) = 0.65, p < 0.001) and HepaRG (r(2) = 0.99, p < 0.001) cells. However, E(max) values generated in hepatocytes were only significantly correlated to those determined in Fa2N-4 (r(2) = 0.33, p = 0.005) and HepaRG cells (r(2) = 0.79, p < 0.001). "Gold standard" cytochrome P450 induction data can be generated using primary human hepatocytes, but a restricted, erratic supply and interdonor variability somewhat restrict routine application within a drug discovery setting. HepaRG cells are a valuable recent addition to the armory of in vitro tools for assessing CYP3A4 induction and seem to be an excellent surrogate of primary cells.

Mechanism-based inhibition of cytochrome P450 enzymes: an evaluation of early decision making in vitro approaches and drug-drug interaction prediction methods.

The ability to use in vitro human cytochrome P450 (CYP) time-dependent inhibition (TDI) data for in vivo drug-drug interaction (DDI) predictions should be viewed as a prerequisite to generating the data. Important terms in making such predictions are k(inact) and K(I) but first-line screening assays typically involve characterisation of an IC(50) value or a time dependent shift in IC(50). In the work presented here, two key screening methods from the scientific literature were appraised both in terms of practicality and quality of k(inact)/K(I) estimation. The utility of TDI screening data in DDI predictions was investigated and particular reference given to a simple DDI simulation model based on a spreadsheet that calculates the systemic exposure of unbound inhibitor drug following the input of human pharmacokinetic parameters. Using several clinical mechanism-based CYP DDI examples, the effectiveness of the approach was assessed and compared to other widely available approaches (a simple algorithm that employs a single in vivo unbound inhibitor concentration, a seven-compartment physiologically based pharmacokinetic (PBPK) model that defines the extent of interaction as a result of hepatic inhibitor concentrations and the commercially available software SimCYP). All the methods gave predictions that compared favourably with the observed DDIs, but various advantages and disadvantages of each were also given full consideration. The new model facilitates rapid sensitivity analysis (parameters can be easily input and altered to give a visual representation of the impact on the active enzyme concentration) and it was therefore used to derive "rules of thumb" demonstrating the relationship between extent of DDI, time-dependent IC(50) and dose for typical acidic and basic drugs. Additionally, a TDI decision tree linking into reactive metabolite investigations is proposed for use in a Drug Discovery setting.

Functional consequences of active hepatic uptake on cytochrome P450 inhibition in rat and human hepatocytes.

A series of cytochrome P450 (P450) inhibition experiments were conducted with four hepatic uptake substrates (AZ3, AZ25, atorvastatin, and pitavastatin) using hepatocytes and recombinant P450s. The uptake was shown to be temperature-dependent and was inhibited by estrone sulfate, signifying an active component. At the lowest concentrations tested, the inhibitors concentrated up to 1000-fold in rat hepatocytes, but demonstrated only 5-fold greater P450 inhibition relative to recombinant rat P450s, indicating high intracellular binding. Inhibitor accumulation was considerably lower in human hepatocytes and an increase in inhibitory potency relative to recombinant human P450s was not obvious. This study highlights several technical and conceptual issues in the study of P450 inhibition mediated by compounds actively transported across the basolateral hepatocyte membrane. Primarily, the incubation medium concentration once the inhibitor has fully accumulated into the hepatocytes rather than the starting medium concentration, along with the extent of intracellular binding, must be considered as a foundation for in vitro-in vivo extrapolations. Additionally, it is suggested that if the K(m) value for the active uptake process is close to the P450 inhibition K(i), hepatocytes may be used only to establish the free drug accumulation ratio at a clinically relevant drug concentration, and this information, along with the (recombinant P450) K(i) value, may be used to simulate the likely impact of active hepatic uptake on P450 inhibition in vivo.