Optimization of the Antibacterial Spectrum and the Developability Profile of the Novel-Class Natural Product Corramycin.

Corramycin 1 is a novel zwitterionic antibacterial peptide isolated from a culture of the myxobacterium Corallococcus coralloides. Though Corramycin displayed a narrow spectrum and modest MICs against sensitive bacteria, its ADMET and physchem profile as well as its high tolerability in mice along with an outstanding in vivo efficacy in an Escherichia coli septicemia mouse model were promising and prompted us to embark on an optimization program aiming at enlarging the spectrum and at increasing the antibacterial activities by modulating membrane permeability. Scanning the peptidic moiety by the Ala-scan strategy followed by key stabilization and introduction of groups such as a primary amine or siderophore allowed us to enlarge the spectrum and increase the overall developability profile. The optimized Corramycin 28 showed an improved mouse IV PK and a broader spectrum with high potency against key Gram-negative bacteria that translated into excellent efficacy in several in vivo mouse infection models.

The antimalarial MMV688533 provides potential for single-dose cures with a high barrier to Plasmodium falciparum parasite resistance.

The emergence and spread of Plasmodium falciparum resistance to first-line antimalarials creates an imperative to identify and develop potent preclinical candidates with distinct modes of action. Here, we report the identification of MMV688533, an acylguanidine that was developed following a whole-cell screen with compounds known to hit high-value targets in human cells. MMV688533 displays fast parasite clearance in vitro and is not cross-resistant with known antimalarials. In a P. falciparum NSG mouse model, MMV688533 displays a long-lasting pharmacokinetic profile and excellent safety. Selection studies reveal a low propensity for resistance, with modest loss of potency mediated by point mutations in PfACG1 and PfEHD. These proteins are implicated in intracellular trafficking, lipid utilization, and endocytosis, suggesting interference with these pathways as a potential mode of action. This preclinical candidate may offer the potential for a single low-dose cure for malaria.

Improving Prediction of Metabolic Clearance Using Quantitative Extrapolation of Results Obtained From Human Hepatic Micropatterned Cocultures Model and by Considering the Impact of Albumin Binding.

The objective was to compare, with the same data set, the predictive performance of 3 in vitro assays of hepatic clearance (CL), namely, micropatterned cocultures (also referring to HepatoPac®) and suspension as well as monolayer hepatocytes to define which assay is the most accurate. Furthermore, existing in vitro-to-in vivo extrapolation (IVIVE) methods were challenged to verify which method is the most predictive (i.e., direct scaling method without binding correction, conventional method based either on the unbound fraction in plasma (fup) according to the free-drug hypothesis, or based on an fup value adjusted for the albumin [ALB]-facilitated hepatic uptake phenomenon). Accordingly, the role of ALB binding was specifically challenged, and consequently, the ALB production was monitored in parallel to the metabolic stability. The ALB concentration data were used to compare the in vitro assays and to adjust the value of fup of each drug to mimic the ALB-facilitated hepatic uptake phenomenon. The results confirmed that the direct and conventional IVIVE methods generally overpredicted and underpredicted the CL in vivo in humans, respectively. However, the underprediction of the conventional IVIVE method based on fup was significantly reduced from data generated with the HepatoPac® system compared with the 2 other in vitro assays, which is possibly because that system is producing ALB at a rate much closer to the in vivo condition in liver. Hence, these observations suggest that the presence of more ALB molecules per hepatocyte in that HepatoPac® system may have facilitated the hepatic uptake of several bound drugs because their intrinsic CL was increased instead of being decreased by the ALB binding effect. Accordingly, the IVIVE method based on the fup value adjusted for the ALB-facilitated uptake phenomenon gave the lowest prediction bias from the statistical analyses. This study indicated that the HepatoPac® system combined with the adjusted value of fup was the most reliable IVIVE method and revealed the importance of quantifying the in vitro-to-in vivo variation of ALB concentration to improve the CL predictions, which would help any future physiologically based pharmacokinetics modeling exercise.

IMI - oral biopharmaceutics tools project - evaluation of bottom-up PBPK prediction success part 1: Characterisation of the OrBiTo database of compounds.

Predicting oral bioavailability (Foral) is of importance for estimating systemic exposure of orally administered drugs. Physiologically-based pharmacokinetic (PBPK) modelling and simulation have been applied extensively in biopharmaceutics recently. The Oral Biopharmaceutical Tools (OrBiTo) project (Innovative Medicines Initiative) aims to develop and improve upon biopharmaceutical tools, including PBPK absorption models. A large-scale evaluation of PBPK models may be considered the first step. Here we characterise the OrBiTo active pharmaceutical ingredient (API) database for use in a large-scale simulation study. The OrBiTo database comprised 83 APIs and 1475 study arms. The database displayed a median logP of 3.60 (2.40-4.58), human blood-to-plasma ratio of 0.62 (0.57-0.71), and fraction unbound in plasma of 0.05 (0.01-0.17). The database mainly consisted of basic compounds (48.19%) and Biopharmaceutics Classification System class II compounds (55.81%). Median human intravenous clearance was 16.9L/h (interquartile range: 11.6-43.6L/h; n=23), volume of distribution was 80.8L (54.5-239L; n=23). The majority of oral formulations were immediate release (IR: 87.6%). Human Foral displayed a median of 0.415 (0.203-0.724; n=22) for IR formulations. The OrBiTo database was found to be largely representative of previously published datasets. 43 of the APIs were found to satisfy the minimum inclusion criteria for the simulation exercise, and many of these have significant gaps of other key parameters, which could potentially impact the interpretability of the simulation outcome. However, the OrBiTo simulation exercise represents a unique opportunity to perform a large-scale evaluation of the PBPK approach to predicting oral biopharmaceutics.

Evaluation of Normalization Methods To Predict CYP3A4 Induction in Six Fully Characterized Cryopreserved Human Hepatocyte Preparations and HepaRG Cells.

Prediction of drug-drug interactions due to cytochrome P450 isoform 3A4 (CYP3A4) overexpression is important because this CYP isoform is involved in the metabolism of about 30% of clinically used drugs from almost all therapeutic categories. Therefore, it is mandatory to attempt to predict the potential of a new compound to induce CYP3A4. Among several in vitro-in vivo extrapolation methods recently proposed in the literature, an approach using a scaling factor, called a d factor, for a given hepatocyte batch to provide extrapolation between in vitro induction data and clinical outcome has been adopted by leading health authorities. We challenged the relevance of the calibration factor determined using a set of 15 well-known clinical CYP3A4 inducers or the potent CYP3A4 inducer rifampicin only. These investigations were conducted using six batches of human hepatocytes and an established HepaRG cell line. Our findings show that use of a calibration factor is preferable for clinical predictions, as shown previously by other investigators. Moreover, the present results also suggest that the accuracy of prediction through calculation of this factor is sufficient when rifampicin is considered alone, and the use of a larger set of fully characterized CYP3A4 clinical inducers is not required. For the established HepaRG cell line, the findings obtained in three experiments using a single batch of cells show a good prediction accuracy with or without the d factor. Additional investigations with different batches of HepaRG cell lines are needed to confirm these results.

CYP3A4-based drug-drug interaction: CYP3A4 substrates' pharmacokinetic properties and ketoconazole dose regimen effect.

The aim of the study was to assess the magnitude of the CYP3A4 inhibitory effect of 2 dosing regimens of ketoconazole and the influence of the pharmacokinetic properties of the CYP3A4 substrate on the extent of the substrate exposure increase. For this purpose, a clinical study was conducted and PBPK modeling simulations were performed. A crossover study was conducted in healthy subjects. The study was designed to compare the effects of different regimens of reversible CYP3A4 inhibitors, i.e., ketoconazole 400 mg OD, ketoconazole 200 mg BID, on two CYP3A4 substrates, alprazolam and midazolam, reflecting different pharmacokinetic properties in terms of first-pass effect and elimination. In parallel, time-based simulations were performed using the Simcyp population-based Simulator to address the usefulness of modeling to assess interaction clinical study design with CYP3A4 substrates. Comparison of the OD versus BID regimens for ketoconazole showed an opposite trend for the 2 substrates: BID (200 mg) dosing regimen provided the maximal clearance inhibition for alprazolam, while it was OD (400 mg) dosing regimen for midazolam. However, these effects are moderate despite the well-known pharmacokinetic differences between these substrates, suggesting that these differences are not enough. In the other way round, these investigations show how two CYP3A4 substrates can be different without leading to a major impact of the ketoconazole dosing regimen. The clinical findings are consistent with the Simcyp predictions, in particular the opposite trend observed with midazolam and alprazolam and the ketoconazole dosing regimen. These clinical investigations showed the influence of the CYP3A4 substrates' pharmacokinetic properties and the relevance of ketoconazole dose regimen on the magnitude of the interaction ratios. In addition, PBPK Simcyp simulations demonstrated how they can be used to help clinical study design assessment to capture the maximum effect.

PBPK modeling of irbesartan: incorporation of hepatic uptake.

Physiological based pharmacokinetic (PBPK) modeling is now commonly used in drug development to integrate human or animal physiological data in order to predict pharmacokinetic profiles. The aim of this work was to construct and refine a PBPK model of irbesartan taking into account its active uptake via OATP1B1/B3 in order to predict more accurately its pharmacokinetic profile using Simcyp(®). The activity and expression of the human hepatocyte transporters OATP1B1 and OATP1B3 were studied. The relative activity factors (RAFs) for OATP1B1 and OATP1B3 transporters were calculated from intrinsic clearances obtained by concentration dependent uptake experiments in human hepatocytes and HEK overexpressing cells: RAF1B1 using estrone-3-sulfate and pitavastatine clearances, and RAF1B3 using cholecystokinine octapeptide (CCK-8) clearances. The relative expression factor (REF) was calculated by comparing immunoblotting of hepatocytes (REFHH ) or tissues (REFtissue) with those of overexpressing HEK cells for each transporter. These scaling factors were applied in a PBPK model of irbesartan using the Simcyp® simulator. Pharmacokinetic simulation using REFHH (1.82 for OATP1B1, 8.03 for OATP1B3) as an extrapolation factor was the closest to the human clinical pharmacokinetic profile of irbesartan. These investigations show the importance of integrating the contribution of the active uptake of a drug in the liver to improve PBPK modeling.

Physiologically based pharmacokinetic modeling for sequential metabolism: effect of CYP2C19 genetic polymorphism on clopidogrel and clopidogrel active metabolite pharmacokinetics.

Clopidogrel is a prodrug that needs to be converted to its active metabolite (clopi-H4) in two sequential cytochrome P450 (P450)-dependent steps. In the present study, a dynamic physiologically based pharmacokinetic (PBPK) model was developed in Simcyp for clopidogrel and clopi-H4 using a specific sequential metabolite module in four populations with phenotypically different CYP2C19 activity (poor, intermediate, extensive, and ultrarapid metabolizers) receiving a loading dose of 300 mg followed by a maintenance dose of 75 mg. This model was validated using several approaches. First, a comparison of predicted-to-observed area under the curve (AUC)0-24 obtained from a randomized crossover study conducted in four balanced CYP2C19-phenotype metabolizer groups was performed using a visual predictive check method. Second, the interindividual and intertrial variability (on the basis of AUC0-24 comparisons) between the predicted trials and the observed trial of individuals, for each phenotypic group, were compared. Finally, a further validation, on the basis of drug-drug-interaction prediction, was performed by comparing observed values of clopidogrel and clopi-H4 with or without dronedarone (moderate CYP3A4 inhibitor) coadministration using a previously developed and validated physiologically based PBPK dronedarone model. The PBPK model was well validated for both clopidogrel and its active metabolite clopi-H4, in each CYP2C19-phenotypic group, whatever the treatment period (300-mg loading dose and 75-mg last maintenance dose). This is the first study proposing a full dynamic PBPK model able to accurately predict simultaneously the pharmacokinetics of the parent drug and of its primary and secondary metabolites in populations with genetically different activity for a metabolizing enzyme.

Clopidogrel pharmacodynamics and pharmacokinetics in the fed and fasted state: a randomized crossover study of healthy men.

Clopidogrel requires CYP450-mediated hepatic metabolism to form its active metabolite (clopi-H4). This randomized, placebo-controlled, crossover study was designed to characterize the effect of a high-fat or standard breakfast on adenosine diphosphate (ADP)-induced platelet aggregation and exposure to unchanged clopidogrel and clopi-H4 following clopidogrel (300-mg loading dose, 75 mg/d for 4 days) in 72 healthy men. At day 5 and as assessed by liquid chromatography-tandem mass spectrometry, unchanged clopidogrel area under the concentration- time curve from 0 to 24 hours (AUC(0-24)) increased 3.32-fold (90% confidence interval [CI], 2.88-3.84), and clopi-H4 AUC(0-24) decreased nonsignificantly by 12% (90% CI, 0.82-0.94) upon administration of clopidogrel with a standard breakfast. The estimated treatment difference in maximum platelet aggregation (MPA) induced by ADP 5 µM and assessed by light transmission aggregometry was 4.7%, with the 90% CI (0.9%-8.5%) contained within the prespecified equipotency range of ±15%. The mean ± standard deviation of day 5 inhibition of platelet aggregation was 49.7% ± 17.2% and 54.0% ± 13.3% in the fed and fasted states, respectively. Despite increased unchanged clopidogrel and slightly decreased clopi-H4 exposure following clopidogrel administration, the numerical increase in MPA in the fed versus fasted state was small and within the prespecified limit of equipotency. These findings confirm that clopidogrel can be taken with or without food.

Effects of omeprazole and genetic polymorphism of CYP2C19 on the clopidogrel active metabolite.

Clopidogrel is an antiplatelet agent widely used in cardiovascular diseases and an inactive prodrug that needs to be converted to an active metabolite in two sequential metabolic steps. Several CYP450 isoforms involved in these two steps have been described, although the relative contribution in vivo of each enzyme is still under debate. CYP2C19 is considered to be the major contributor to active metabolite formation. In the current study, net CYP2C19 contribution to the active metabolite formation was determined from exposure of the active metabolite in two clinical studies (one phase I study with well balanced genetic polymorphic populations and a meta-analysis with a total of 396 healthy volunteers) at different clopidogrel doses. CYP2C19 involvements were estimated to be from 58 to 67% in intermediate metabolizers (IMs), from 58 to 72% in extensive metabolizers (EMs), and from 56 to 74% in ultrarapid metabolizers (UMs), depending on the study and the dose. For this purpose, a static model was proposed to estimate the net contribution of a given enzyme to the secondary metabolite formation. This static model was compared with a dynamic approach (Simcyp model) and showed good consistency. In parallel, in vitro investigations showed that omeprazole is a mechanism-based inhibitor of CYP2C19 with K(I) of 8.56 µM and K(inact) of 0.156 min(-1). These values were combined with the net CYP2C19 contribution to the active metabolite formation, through a static approach, to predict the inhibitory effect at 80-mg omeprazole doses in EM, IM, and UM CYP2C19 populations, with good consistency, compared with observed clinical values.

Metabolic-based drug-drug interactions prediction, recent approaches for risk assessment along drug development.

Prediction of in vivo drug-drug interactions (DDIs) from in vitro and in vivo data, also named in vitro in vivo extrapolation (IVIVE), is of interest to scientists involved in the discovery and development of drugs. To avoid detrimental DDIs in humans, new drug candidates should be evaluated for their possible interaction with other drugs as soon as possible, not only as an inhibitor or inducer (perpetrator) but also as a substrate (victim). DDI risk assessment is addressed along the drug development program through an iterative process as the features of the new compound entity are revealed. Both in vitro and preclinical/clinical outcomes are taken into account to better understand the behavior of the developed compound and to refine DDI predictions. During the last decades, several equations have been proposed in the literature to predict DDIs, from a quantitative point of view, showing a substantial improvement in the ability to predict metabolism-based in vivo DDIs. Mechanistic and dynamic approaches have been proposed to predict the magnitude of metabolic-based DDIs. The purpose of this article is to provide an overview of the current equations and methods, the pros and cons of each method, the required input data for each of them, as well as the mechanisms (i.e., reversible inhibition, mechanism-based inhibition, induction) underlying metabolic-based DDIs. In particular, this review outlines how the methods (static and dynamic) can be used in a complementary manner during drug development. The discussion of the limitations and advantages associated with the various approaches, as well as regulatory requirements in that field, can give the reader a helpful overview of this growing area.

Pharmacokinetic assessment of a five-probe cocktail for CYPs 1A2, 2C9, 2C19, 2D6 and 3A.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT: * Numerous cocktails using concurrent administration of several cytochrome P450 (CYP) isoform-selective probe drugs have been reported to investigate drug-drug interactions in vivo. * This approach has several advantages: characterize the inhibitory or induction potential of compounds in development toward the CYP enzymes identified in vitro in an in vivo situation, assess several enzymes in the same trial, and have complete in vivo information about potential CYP-based drug interactions. WHAT THIS STUDY ADDS: * This study describes a new cocktail containing five probe drugs that has never been published. * This cocktail can be used to test the effects of a new chemical entity on multiple CYP isoforms in a single clinical study: CYP1A2 (caffeine), CYP2C9 (warfarin), CYP2C19 (omeprazole), CYP2D6 (metoprolol), and CYP3A (midazolam) and was designed to overcome potential liabilities of other reported cocktails. AIMS: To assess the pharmacokinetics (PK) of selective substrates of CYP1A2 (caffeine), CYP2C9 (S-warfarin), CYP2C19 (omeprazole), CYP2D6 (metoprolol) and CYP3A (midazolam) when administered orally and concurrently as a cocktail relative to the drugs administered alone. METHODS: This was an open-label, single-dose, randomized, six-treatment six-period six-sequence William's design study with a wash-out of 7 or 14 days. Thirty healthy male subjects received 100 mg caffeine, 100 mg metoprolol, 0.03 mg kg(-1) midazolam, 20 mg omeprazole and 10 mg warfarin individually and in combination (cocktail). Poor metabolizers of CYP2C9, 2C19 and 2D6 were excluded. Plasma samples were obtained up to 48 h for caffeine, metoprolol and omeprazole, 12 h for midazolam, 312 h for warfarin and the cocktail. Three different validated liquid chromatography tandem mass spectrometry methods were used. Noncompartmental PK parameters were calculated. Log-transformed C(max), AUC(last) and AUC for each analyte were analysed with a linear mixed effects model with fixed term for treatment, sequence and period, and random term for subject within sequence. Point estimates (90% CI) for treatment ratios (individual/cocktail) were computed for each analyte C(max), AUC(last) and AUC. RESULTS: There was no PK interaction between the probe drugs when administered in combination as a cocktail, relative to the probes administered alone, as the 90% CI of the PK parameters was within the prespecified bioequivalence limits of 0.80, 1.25. CONCLUSION: The lack of interaction between probes indicates that this cocktail could be used to evaluate the potential for multiple drug-drug interactions in vivo.

Quantitative correlations among CYP3A sensitive substrates and inhibitors: literature analysis.

As a follow-up to the new classification of CYP3A inhibitors, the present work was undertaken to search for quantitative correlations of AUC ratios between sensitive substrates and midazolam (reference). A large set of clinical studies was obtained utilizing the M&T Drug Interaction Database, and recent Product Labels. Linear relationships were found between midazolam and four CYP3A substrates: simvastatin, buspirone, triazolam and eplerenone. Simvastatin and buspirone were consistently more sensitive than midazolam, independent of the inhibitor. Quantitative correlations of AUC ratios between four CYP3A inhibitors (fluconazole, erythromycin, verapamil, diltiazem) and ketoconazole (400 mg/day) were also uncovered. The average potencies of these inhibitors relative to ketoconazole were 27% for erythromycin, 17% for fluconazole and 19% for verapamil.