Multimodal non-linear optical imaging for the investigation of drug nano-/microcrystal–cell interactions

Drug nano-/microcrystals are being used for sustained parenteral drug release, but safety and efficacy concerns persist as the knowledge of the in vivo fate of long-living particulates is limited. There is a need for techniques enabling the visualization of drug nano-/microcrystals in biological matrices. The aim of this work was to explore the potential of coherent anti-Stokes Raman scattering (CARS) microscopy, supported by other non-linear optical methods, as an emerging tool for the investigation of cellular and tissue interactions of unlabeled and non-fluorescent nano-/microcrystals. Raman and CARS spectra of the prodrug paliperidone palmitate (PP), paliperidone (PAL) and several suspension stabilizers were recorded. PP nano-/microcrystals were incubated with RAW 264.7 macrophages in vitro and their cellular disposition was investigated using a fully-integrated multimodal non-linear optical imaging platform. Suitable anti-Stokes shifts (CH stretching) were identified for selective CARS imaging. CARS microscopy was successfully applied for the selective three-dimensional, non-perturbative and real-time imaging of unlabeled PP nano-/microcrystals having dimensions larger than the optical lateral resolution of approximately 400 nm, in relation to the cellular framework in cell cultures and ex vivo in histological sections. In conclusion, CARS microscopy enables the non-invasive and label-free imaging of (sub)micron-sized (pro-)drug crystals in complex biological matrices and could provide vital information on poorly understood nano-/microcrystal–cell interactions in future.     

Evaluation of drug–drug interactions with fesoterodine

Purpose  To assess drug–drug interactions of fesoterodine with cytochrome P450 (CYP) 3A4 inhibitor (ketoconazole), inducer (rifampicin), and substrates (ethinylestradiol and levonorgestrel). Methods  Effects of ketoconazole 200 mg twice daily and rifampicin 600 mg twice daily on fesoterodine 8 mg once daily were investigated in CYP2D6 extensive metabolizers (EMs) and poor metabolizers (PMs) based on 5-hydroxymethyl tolterodine (5-HMT) pharmacokinetics (principal active fesoterodine metabolite and CYP3A4 substrate). Effects of fesoterodine 8 mg versus placebo once daily on ethinylestradiol and levonorgestrel were investigated based on oral contraceptive pharmacokinetics and on pharmacodynamic effects on progesterone, luteinizing hormone, follicle-stimulating hormone, and estradiol plasma levels. Results  Compared with fesoterodine alone, coadministration of fesoterodine with ketoconazole resulted in increases in mean 5-HMT maximum concentration in plasma (C_max; from 3.0 to 6.0 ng/mL in EMs and from 6.4 to 13.4 ng/mL in PMs) and mean area under the plasma concentration time curve (AUC; from 38.2 to 88.3 ng h/mL in EMs and 88.3 to 217.2 ng h/mL in PMs). Coadministration of festerodine with rifampicin resulted in decreases in mean 5-HMT C_max (from 5.2 to 1.5 ng/mL in EMs and from 6.8 to 1.9 ng/mL in PMs) and mean AUC (from 62.4 to 14.4 ng h/mL in EMs and from 87.8 to 19.6 ng h/mL in PMs). Fesoterodine did not affect oral contraceptive pharmacokinetics or pharmacodynamics or the suppression of ovulation. Conclusions  Fesoterodine dosage should not exceed 4 mg once daily when taken concomitantly with potent CYP3A4 inhibitors. Coadministration of CYP3A4 inducers with fesoterodine may produce subtherapeutic 5-HMT exposures. No dose adjustment is necessary for concomitant use of fesoterodine with oral contraceptives. Funding for this study was provided by Schwarz Biosciences GmbH, and Pfizer Inc.     

Improvement in the handling of drug–drug interactions

Approximately one in 200 hospitalised patients has a serious adverse drug effect caused by drug–drug interactions (DDIs). Such adverse effects should be avoidable, but current information provided on DDIs is often incomplete and difficult or even impossible to translate into true risk and appropriate tangible action. Clinicians need to know the mean and maximal expected effect of a DDI on clinical endpoints, any dose adjustments required, and how to monitor tolerability and efficacy in patients subject to a DDI. To this end, improved study designs should take the objective of improving treatment explicitly into account, and any existing DDI data should be publicly accessible. Modelling needs to be used more extensively in order to quantitatively predict the effects of DDIs on clinical endpoints in patients and to relate clinical endpoint effects considered as acceptable to respective changes in experimental and clinical studies. Computer-based expert systems will be required to convert such DDI data into recommendations applicable to the individual patient. Therefore, the incorporation of DDIs in a more general procedure for personalisation of drug therapy is desirable.     

Consistency of psychotropic drug–drug interactions listed in drug monographs

Background

With an increasing prevalence of psychotropic polypharmacy, clinicians depend on drug–drug interaction (DDI) references to ensure safe regimens, but the consistency of such information is frequently questioned.

Physiologically based pharmacokinetic modelling of oxycodone drug–drug interactions

Oxycodone is an opioid analgesic with several pharmacologically active metabolites and relatively narrow therapeutic index. Cytochrome P450 (CYP) 3A4 and CYP2D6 play major roles in the metabolism of oxycodone and its metabolites. Thus, inhibition and induction of these enzymes may result in substantial changes in the exposure of both oxycodone and its metabolites. In this study, a physiologically based pharmacokinetic (PBPK) model was built using GastroPlus™ software for oxycodone, two primary metabolites (noroxycodone, oxymorphone) and one secondary metabolite (noroxymorphone). The model was built based on literature and in house in vitro and in silico data. The model was refined and verified against literature clinical data after oxycodone administration in the absence of drug–drug interactions (DDI). The model was further challenged with simulations of oxycodone DDI with CYP3A4 inhibitors ketoconazole and itraconazole, CYP3A4 inducer rifampicin and CYP2D6 inhibitor quinidine. The magnitude of DDI (AUC ratio) was predicted within 1.5-fold error for oxycodone, within 1.8-fold and 1.3–4.5-fold error for the primary metabolites noroxycodone and oxymorphone, respectively, and within 1.4–4.5-fold error for the secondary metabolite noroxymorphone, when compared to the mean observed AUC ratios. This work demonstrated the capability of PBPK model to simulate DDI of the administered compounds and the formed metabolites of both DDI victim and perpetrator. However, the predictions for the formed metabolites tend to be associated with higher uncertainty than the predictions for the administered compound. The oxycodone model provides a tool for forecasting oxycodone DDI with other CYP3A4 and CYP2D6 DDI perpetrators that may be co-administered with oxycodone.     

Does inhibition of P-glycoprotein lead to drug–drug interactions?

Permeability-glycoprotein (Pgp) is a drug transporter responsible for the efflux of xenobiotics out of cells that influence the pharmacokinetics of numerous drugs. However, the role of this transporter in drug-drug interactions is still poorly studied even though a lot of P-glycoprotein substrates and P-glycoprotein inhibitors are identified among drugs of standard usage. On one hand, Pgp is distributed within a lot of organs and tissues implicated in the absorption or excretion of xenobiotics, and drug-drug interactions with this protein may increase the bioavailability of simultaneously administered active drugs. On the other hand, Pgp is linked to the integrity of blood-tissue barriers, such as the blood-brain barrier or placenta, and a partial blockage of Pgp could be responsible for a new drug distribution in the organism with possible increase of drug rates in organs behind these barriers. Therefore, concomitant administration of substrates and Pgp inhibitors would modify drug pharmacokinetics by increasing bioavailability and organ uptake, leading to more adverse drug reactions and toxicities. Consequently, the identification and comprehension of these drug-drug interactions remain important keys to risk assessment.     

Comparison of different approaches to predict metabolic drug–drug interactions

Three approaches were compared to predict the actual magnitude of drug interaction (the mean fold-change in the area under the curve (AUC)) of reversible or irreversible (mechanism-based) cytochrome P450 (CYP) inhibitors. These were: (1) the pragmatic use of the ‘[I]/K_i’ approach; (2) the ‘Mechanistic-Static Model’ (MSM), which is a more complex extension of the ‘[I]/K_i’ approach that incorporates f_m,CYP, intestinal availability for CYP3A substrates, and mechanism-based inhibition (MBI); and (3) the ‘Mechanistic-Dynamic Model’ (MDM) which considers the time-variant change in the concentration of the inhibitor by using physiologically-based pharmacokinetic (PBPK) models (as implemented within the Simcyp® Population-Based ADME Simulator). The three approaches ([I]/K_i, MSM, and MDM) predicted a ‘correct’ drug–drug interaction (DDI) result (interaction: Greater than or equal to twofold; no interaction: Less than twofold) in 74, 87, and 80% of the 100 trials evaluated, respectively. Importantly, for trials with a greater than or equal to twofold change in AUC in the presence of the inhibitor (59 trials), the [I]/K_i, MSM, and MDM approaches predicted the mean AUC change within twofold of actual in 17, 53, and 64% of the trials, respectively. Overall, the MDM approach showed an improvement in the prediction of DDI magnitude compared to the other methods evaluated and was useful in its ability to predict variability in DDI magnitude and pharmacokinetic parameters. Moreover, the MDM model allowed the automated prediction of the inhibition of parallel metabolic pathways and simulations of different dosing regimens.     

Assessment of non-linear combination effect terms for drug–drug interactions

Drugs interact with their targets in different ways. A diversity of modeling approaches exists to describe the combination effects of two drugs. We investigate several combination effect terms (CET) regarding their underlying mechanism based on drug-receptor binding kinetics, empirical and statistical summation principles and indirect response models. A list with properties is provided and the interrelationship of the CETs is analyzed. A method is presented to calculate the optimal drug concentration pair to produce the half-maximal combination effect. This work provides a comprehensive overview of typically applied CETs and should shed light into the question as to which CET is appropriate for application in pharmacokinetic/pharmacodynamic models to describe a specific drug–drug interaction mechanism.     

Epidemiology and characteristics of adverse drug reactions caused by drug–drug interactions

Introduction: Drug–drug interactions (DDIs) arise in numerous different ways, involving pharmacokinetic or pharmacodynamic mechanisms. Adverse drug reactions are a possible consequence of DDIs and health operators are often unaware of the clinical risks of certain drug combinations. Many papers on drug interactions have been published in recent years, but most of them focused on potential DDIs while few studies have been conducted on actual interactions.

Areas covered: This paper reviews the epidemiology of actual DDIs in outpatients as well as in hospital settings and in spontaneous reporting databases. The incidence of actual DDIs is consistently lower than that of potential DDIs. However, the absolute number of patients involved is high, representing a significant proportion of adverse drug reactions. The importance of risk factors such as age, polypharmacy and genetic polymorphisms is also evaluated. The relevance and efficacy of tools for recognizing and preventing DDIs are discussed.

Expert opinion: Potential DDIs far outnumber actual drug interactions. The potential for an adverse interaction to occur is often theoretical, and clinically important adverse effects occur only in the presence of specific risk factors. Several studies have shown the efficacy of computers in early detection of DDIs. However, a correct risk–benefit evaluation by the prescribing physician, together with a careful clinical, physiological and biochemical monitoring of patients, is essential. Future directions of drug interaction research include the increasing importance of pharmacogenetics in preventing DDIs and the evaluation of interactions with biological drugs.     

Polymer–drug interactions and wetting of solid dispersions

We demonstrate the ability of drugs to influence the wetting of solid dispersion tablets in unexpected ways. Five model drugs of different water solubility and ability to interact with the involved polymers were incorporated in hydrophilic polymer matrices, made of either hydroxypropyl methylcellulose (HPMC) or polyvinyl pyrrolidone (PVP). The physical mixtures of all combinations of drug and polymer presented surface hydrophobicities, as measured by the equilibrium advancing contact angle of water, which are expected for materials that do not influence the interactions of each other with water. However, the solid dispersions containing HPMC deviated from this regular behaviour and displayed contact angles below those of the pure compounds involved, either drug or polymer. This behaviour is explained by changed surface exposure of HPMC side groups, as a result of changes in intermolecular hydrogen bonds. In addition to water contact angle measurements, we employed NMR imaging to monitor the time course of water ingress and swelling.     

Pharmacokinetic drug–drug interactions with methotrexate in oncology

Methotrexate is an antifolate agent used in the treatment of various cancers and some autoimmune diseases. In oncology, methotrexate is frequently administered at a high dose (>1 g/m²) and comes with various procedures to reduce the occurrence of toxicity and particularly to ensure optimal renal elimination. Drug–drug interactions involving methotrexate are the origin of severe side effects owing to delayed elimination of the antifolate and, more rarely, of decreased efficacy in relation to suboptimal exposure. Most of these interactions are driven by membrane drug transporters whose activity/expression can be inhibited by the interacting medication. In the last 10 years, research on drug transporters has permitted retrospective identification of the molecular mechanisms underlying drug–drug interactions with methotrexate. This article summarizes reported drug–drug interactions involving methotrexate in clinical oncology with reference to the role of drug transporters that control the disposition of the antifolate agent.     

A critical evaluation of clinical decision support for the detection of drug–drug interactions

Introduction: Incorporation of clinical decision support systems (CDSSs) into computerized physician order entry assists prescribers with medication dosing, identification of duplicate therapies, drug-allergy alerts and drug–drug interactions (DDIs). The generation of DDI alerts is one aspect of CDSS that may improve patient safety and reduce adverse drug events.

Areas covered: Currents issues with the generation of DDI alerts, such as alert fatigue, unclear clinical significance and database inconsistencies are a few of the problems that have been identified with DDI alerting. Research has shown that DDI alerting may be improved through the tiering of alerts, generation of patient-specific alert and directing some alerts to clinicians other than physicians. More research in this area, such as how to decrease the variability of database rating systems, improve the identification of clinically significant alerts and increase the patient specificity of the generated DDI alerts, should be conducted.

Expert opinion: DDI knowledgebases need to take into account more patient-specific information. Strategies to avoid alert fatigue, such as DDI tiering and reducing signal:noise ratios, are important areas for future study. End-user participation and clinician feedback should be incorporated in the development of DDI knowledgebases to increase alert compliance.     

Herb–drug interactions: an overview of systematic reviews

OBJECTIVES The aim of this overview of systematic reviews (SRs) is to evaluate critically the evidence regarding interactions between herbal medicinal products (HMPs) and synthetic drugs.METHODS Four electronic databases were searched to identify relevant SRs.RESULTS Forty‐six SRs of 46 different HMPs met our inclusion criteria. The vast majority of SRs were of poor methodological quality. The majority of these HMPs were not associated with severe herb–drug interactions. Serious herb–drug interactions were noted for Hypericum perforatum and Viscum album. The most severe interactions resulted in transplant rejection, delayed emergence from anaesthesia, cardiovascular collapse, renal and liver toxicity, cardiotoxicity, bradycardia, hypovolaemic shock, inflammatory reactions with organ fibrosis and death. Moderately severe interactions were noted for Ginkgo biloba, Panax ginseng, Piper methysticum, Serenoa repens and Camellia sinensis. The most commonly interacting drugs were antiplatelet agents and anticoagulants.CONCLUSION The majority of the HMPs evaluated in SRs were not associated with drug interactions with serious consequences. However, the poor quality and the scarcity of the primary data prevent firm conclusions.     

The incidence of potential drug–drug interactions in elderly patients with arterial hypertension

Objective To assess the incidence and type of potential, clinically significant drug–drug interactions in elderly outpatients with arterial hypertension. Setting Three community pharmacies in Croatia. Method Eligible patients were aged 65 or older, treated for arterial hypertension and received 2 or more drugs. Potential interactions were identified by Lexi-Interact software. The software categorized each potential interaction according to clinical significance in five groups: (A) No known interaction; (B) Specified agents may interact, but there is little to no evidence of clinical concern; (C) Specified agents may interact in a clinically significant manner. Monitoring therapy is suggested; (D) The two medications may interact in a clinically significant manner. Modification of therapy is suggested; (X) Contraindicated combination. Interactions of level C, D and X were considered clinically significant. Main outcome measure The incidence and type of potential drug–drug interactions. Results There were 265 patients included in the study. Potential, clinically significant drug interactions were identified in 240 (90.6%) patients, out of which 97.9% had interactions with clinical significance C, 20.4% D, and 0.8% X. The median number of drug interactions per patient was 4. We identified 215 drug combinations with the potential to cause clinically significant interaction, out of which 83.3% had clinical significance C, 16.3% clinical significance D, and 0.4% clinical significance X. Conclusion Drug–drug interactions are common in elderly hypertensive patients. Computer-based screening could help pharmacists and physicians to recognize potential, clinically significant interactions.     

Human hepatocyte assessment of imatinib drug–drug interactions – complexities in clinical translation

Aim

Inducers and inhibitors of CYP3A, such as ritonavir and efavirenz, may be used as part of the highly active antiretroviral therapy (HAART) to treat HIV patients. HIV patients with chronic myeloid leukemia or gastrointestinal stromal tumour may need imatinib, a CYP3A4 substrate with known exposure response–relationships. Administration of imatinib to patients on ritonavir or efavirenz may result in altered imatinib exposure leading to increased toxicity or failure of therapy, respectively. We used primary human hepatocyte cultures to evaluate the magnitude of interaction between imatinib and ritonavir/efavirenz.

Methods

Hepatocytes were pre-treated with vehicle, ritonavir, ketoconazole, efavirenz or rifampicin, and the metabolism of imatinib was characterized over time. Concentrations of imatinib and metabolite were quantitated in combined lysate and medium, using LC-MS.

Results

The predicted changes in imatinib CL_oral (95% CI) with ketoconazole, ritonavir, rifampicin and efavirenz were 4.0-fold (0, 9.2) lower, 2.8-fold (0.04, 5.5) lower, 2.9-fold (2.2, 3.5) higher and 2.0-fold (0.42, 3.5) higher, respectively. These predictions were in good agreement with clinical single dose drug–drug interaction studies, but not with reports of imatinib interactions at steady-state. Alterations in metabolism were similar after acute or chronic imatinib exposure.

Conclusions

In vitro human hepatocytes predicted increased clearance of imatinib with inducers and decreased clearance with inhibitors of CYP enzymes. The impact of HAART on imatinib may depend on whether it is being initiated or has already been dosed chronically in patients. Therapeutic drug monitoring may have a role in optimizing imatinib therapy in this patient population.     

Assessment of liver slices for research on metabolic drug–drug interactions in cattle

1.?Precision-cut liver slices (PCLS) from food-producing animals have not been extensively used to study xenobiotic metabolism, and thus information on this field of research is sparse.

2.?The aims of the present work were to further validate the technique of production and culture of bovine PCLS and to characterize the metabolic interaction between the anthelmintic albendazole (ABZ) and the flavin-monooxygenase (FMO) inhibitor methimazole (MTZ).

3.?Nine steers were used as donors. PCLS were produced and incubated under two methods: a dynamic organ culture (DOC) incubator and a well-plate (WP) system.

4.?Tissue viability, assessed through both structural and functional markers, was preserved throughout 12?h of incubation. ABZ was metabolized to its (+) and (-) albendazole sulfoxide stereoisomers (ABZSO) in bovine PCLS. The interaction between ABZ and MTZ resulted in a reduction (p?p?5.?Both culture systems were suitable for assessing the interaction between ABZ and MTZ.

6.?Overall, the results presented herein show that PCLS are a useful and reliable tool for short-term studies on metabolic drug–drug interactions in the bovine species.     

Development of an ADME and drug–drug interactions knowledge database for the acceleration of drug discovery and development

It is widely recognised that predicting or determining the absorption, distribution, metabolism and excretion (ADME) properties of a compound as early as possible in the drug discovery process helps to prevent costly late-stage failures. Although in recent years high-throughput in vitro absorption distribution metabolism excretion toxicity (ADMET) screens have been implemented, more efficient in silico filters are still highly needed to predict and model the most relevant metabolic and pharmacokinetic end points, and thereby accelerate drug discovery and development. The usefulness of the data generated and published for the chemist, biologist or project manager who ultimately wants to understand and optimise the ADME properties of lead compounds cannot be argued with. Collecting and comparing data is an overwhelming task for the time-pressed scientist. Aureus Pharma provides a uniquely specialised solution for knowledge generation in drug discovery. AurSCOPE^® ADME/DDI (drug–drug interaction) is a fully annotated, structured knowledge database containing all the pertinent biological and chemical information on the metabolic properties of drugs. This Aureus knowledge database has proven to be highly useful in designing predictive models and identifying potential drug–drug interactions.