Single concentration loss of activity assay provides an improved assessment of drug-drug interaction risk compared to IC50-shift

1. The utility of two abbreviated, higher-throughput assays [IC50-shift and the loss of activity (LOA) assay] to evaluate time-dependent inhibition (TDI) of 24 structurally related compounds was compared. 2. Good correlation (R(2)  = 0.90) between % inhibition and kinact/KI suggested that the LOA assay has utility as an indicator of TDI potential. Weaker correlation was observed for the shifted IC50 (IC50(T = 30)) (R(2) = 0.61) and the fold-shift in IC50 (R(2) = 0.17). 3. Primary mechanism for poor correlation was depletion of active enzyme at concentrations > 1 μM leading to greater than predicted inhibition in the IC50-shift assay. 4. Previously reported strong correlations between IC50(T = 30) and kinact/KI were found to be dependent on potent TDI compounds with kinact/KI > 30; correlation was reduced for moderate inhibitors (kinact/KI < 30). LOA assay maintained good correlation even when strong TDI compounds were excluded. 5. LOA assay (% Inhibition at 30 min, 10 μM) was a good predictor of in vivo DDI (AUCr), providing a graded response with low potential for false negatives or positives. IC50-shift assay had bias for over-predicting in vivo DDI and was more likely to identify false positives.

Probing Mechanisms of CYP3A Time-Dependent Inhibition Using a Truncated Model System

Time-dependent inhibition (TDI) of cytochrome P450 (CYP) enzymes may incur serious undesirable drug-drug interactions and in rare cases drug-induced idiosyncratic toxicity. The reactive metabolites are often generated through multiple sequential biotransformations and form adducts with CYP enzymes to inactivate their function. The complexity of these processes makes addressing TDI liability very challenging. Strategies to mitigate TDI are therefore highly valuable in discovering safe therapies to benefit patients. In this Letter, we disclose our simplified approach toward addressing CYP3A TDI liabilities, guided by metabolic mechanism hypotheses. By adding a methyl group onto the α carbon of a basic amine, TDI activities of both the truncated and full molecules (7a and 11) were completely eliminated. We propose that truncated molecules, albeit with caveats, may be used as surrogates for full molecules to investigate TDI.

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.

Three- and four-class classification models for P-glycoprotein inhibitors using counter-propagation neural networks

P-glycoprotein (P-gp) is an ATP binding cassette (ABC) transporter that helps to protect several certain human organs from xenobiotic exposure. This efflux pump is also responsible for multi-drug resistance (MDR), an issue of the chemotherapy approach in the fight against cancer. Therefore, the discovery of P-gp inhibitors is considered one of the most popular strategies to reverse MDR in tumour cells and to improve therapeutic efficacy of commonly used cytotoxic drugs. Until now, several generations of P-gp inhibitors have been developed but they have largely failed in preclinical and clinical studies due to lack of selectivity, poor solubility and severe pharmacokinetic interactions. In this study, three models (SION, SIO, SIN) to classify specific 'true' P-gp inhibitors as well as three other models (CPBN, CPB1, CPN) to distinguish between P-gp inhibitors, CYP 3A inhibitors and co-inhibitors of these proteins with rather high accuracy values for the test set and the external set were generated based on counter-propagation neural networks (CPG-NN). Such three and four-class classification models helped provide more information about the bioactivities of compounds not only on one target (P-gp), but also on a combination of multiple targets (P-gp, CYP 3A).

A numerical method for analysis of in vitro time-dependent inhibition data. Part 1. Theoretical considerations

Inhibition of cytochromes P450 by time-dependent inhibitors (TDI) is a major cause of clinical drug-drug interactions. It is often difficult to predict in vivo drug interactions based on in vitro TDI data. In part 1 of these manuscripts, we describe a numerical method that can directly estimate TDI parameters for a number of kinetic schemes. Datasets were simulated for Michaelis-Menten (MM) and several atypical kinetic schemes. Ordinary differential equations were solved directly to parameterize kinetic constants. For MM kinetics, much better estimates of KI can be obtained with the numerical method, and even IC50 shift data can provide meaningful estimates of TDI kinetic parameters. The standard replot method can be modified to fit non-MM data, but normal experimental error precludes this approach. Non-MM kinetic schemes can be easily incorporated into the numerical method, and the numerical method consistently predicts the correct model at errors of 10% or less. Quasi-irreversible inactivation and partial inactivation can be modeled easily with the numerical method. The utility of the numerical method for the analyses of experimental TDI data is provided in our companion manuscript in this issue of Drug Metabolism and Disposition (Korzekwa et al., 2014b).

Reversible mechanisms of enzyme inhibition and resulting clinical significance

Inhibition of a drug-metabolizing enzyme by the reversible interaction of a drug with the enzyme, thus decreasing the metabolism of another drug, is a major cause of clinically significant drug-drug interactions. This chapter defines the four reversible mechanisms of inhibition exhibited by drugs: competitive, noncompetitive, uncompetitive, and mixed competitive/noncompetitive. An in vitro procedure to determine the potential of a drug to be a reversible inhibitor is also provided. Finally, a number of examples of clinically significant drug-drug interactions resulting from reversible inhibition are described.

Discovery of 4-{4-[(3R)-3-Methylmorpholin-4-yl]-6-[1-(methylsulfonyl)cyclopropyl]pyrimidin-2-yl}-1H-indole (AZ20): a potent and selective inhibitor of ATR protein kinase with monotherapy in vivo antitumor activity

ATR is an attractive new anticancer drug target whose inhibitors have potential as chemo- or radiation sensitizers or as monotherapy in tumors addicted to particular DNA-repair pathways. We describe the discovery and synthesis of a series of sulfonylmorpholinopyrimidines that show potent and selective ATR inhibition. Optimization from a high quality screening hit within tight SAR space led to compound 6 (AZ20) which inhibits ATR immunoprecipitated from HeLa nuclear extracts with an IC50 of 5 nM and ATR mediated phosphorylation of Chk1 in HT29 colorectal adenocarcinoma tumor cells with an IC50 of 50 nM. Compound 6 potently inhibits the growth of LoVo colorectal adenocarcinoma tumor cells in vitro and has high free exposure in mouse following moderate oral doses. At well tolerated doses 6 leads to significant growth inhibition of LoVo xenografts grown in nude mice. Compound 6 is a useful compound to explore ATR pharmacology in vivo.

Preclinical strategy to reduce clinical hepatotoxicity using in vitro bioactivation data for >200 compounds

Drug-induced liver injury is the most common cause of market withdrawal of pharmaceuticals, and thus, there is considerable need for better prediction models for DILI early in drug discovery. We present a study involving 223 marketed drugs (51% associated with clinical hepatotoxicity; 49% non-hepatotoxic) to assess the concordance of in vitro bioactivation data with clinical hepatotoxicity and have used these data to develop a decision tree to help reduce late-stage candidate attrition. Data to assess P450 metabolism-dependent inhibition (MDI) for all common drug-metabolizing P450 enzymes were generated for 179 of these compounds, GSH adduct data generated for 190 compounds, covalent binding data obtained for 53 compounds, and clinical dose data obtained for all compounds. Individual data for all 223 compounds are presented here and interrogated to determine what level of an alert to consider termination of a compound. The analysis showed that 76% of drugs with a daily dose of <100 mg were non-hepatotoxic (p < 0.0001). Drugs with a daily dose of ≥100 mg or with GSH adduct formation, marked P450 MDI, or covalent binding ≥200 pmol eq/mg protein tended to be hepatotoxic (∼ 65% in each case). Combining dose with each bioactivation assay increased this association significantly (80-100%, p < 0.0001). These analyses were then used to develop the decision tree and the tree tested using 196 of the compounds with sufficient data (49% hepatotoxic; 51% non-hepatotoxic). The results of these outcome analyses demonstrated the utility of the tree in selectively terminating hepatotoxic compounds early; 45% of the hepatotoxic compounds evaluated using the tree were recommended for termination before candidate selection, whereas only 10% of the non-hepatotoxic compounds were recommended for termination. An independent set of 10 GSK compounds with known clinical hepatotoxicity status were also assessed using the tree, with similar results.

Statistical methods for analysis of time-dependent inhibition of cytochrome p450 enzymes

Time-dependent inhibition (TDI) of cytochrome P450 (P450) enzymes, especially CYP3A4, is an important attribute of drugs in evaluating the potential for pharmacokinetic drug-drug interactions. The analysis of TDI data for P450 enzymes can be challenging, yet it is important to be able to reliably evaluate whether a drug is a TDI or not, and if so, how best to derive the inactivation kinetic parameters K(I) and k(inact). In the present investigation a two-step statistical evaluation was developed to evaluate CYP3A4 TDI data. In the first step, a two-sided two-sample z-test is used to compare the k(obs) values measured in the absence and presence of the test compound to answer the question of whether the test compound is a TDI or not. In the second step, k(obs) values are plotted versus both [I] and ln[I] to determine whether a significant correlation exists, which can then inform the investigator of whether the inactivation kinetic parameters, K(I) and k(inact), can be reliably estimated. Use of this two-step statistical evaluation is illustrated with the examination of five drugs of varying capabilities to inactivate CYP3A4: ketoconazole, erythromycin, raloxifene, rosiglitazone, and pioglitazone. The use of a set statistical algorithm offers a more robust and objective approach to the analysis of P450 TDI data than frequently employed empirically derived or heuristic approaches.

Discovery of novel PI3-kinase δ specific inhibitors for the treatment of rheumatoid arthritis: taming CYP3A4 time-dependent inhibition

PI3Kδ is a lipid kinase and a member of a larger family of enzymes, PI3K class IA(α, β, δ) and IB (γ), which catalyze the phosphorylation of PIP2 to PIP3. PI3Kδ is mainly expressed in leukocytes, where it plays a critical, nonredundant role in B cell receptor mediated signaling and provides an attractive opportunity to treat diseases where B cell activity is essential, e.g., rheumatoid arthritis. We report the discovery of novel, potent, and selective PI3Kδ inhibitors and describe a structural hypothesis for isoform (α, β, γ) selectivity gained from interactions in the affinity pocket. The critical component of our initial pharmacophore for isoform selectivity was strongly associated with CYP3A4 time-dependent inhibition (TDI). We describe a variety of strategies and methods for monitoring and attenuating TDI. Ultimately, a structure-based design approach was employed to identify a suitable structural replacement for further optimization.