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Quantitative evaluation of potential pharmacodynamic (PD) interactions is important in tuberculosis drug development in order to optimize Phase 2b drug selection and ultimately to define clinical combination regimens. In this work, we used simulations to (1) evaluate different analysis methods for detecting PD interactions between two hypothetical anti-tubercular drugs in in vitro time-kill experiments, and (2) provide design recommendations for evaluation of PD interactions. The model used for all simulations was the Multistate Tuberculosis Pharmacometric (MTP) model linked to the General Pharmacodynamic Interaction (GPDI) model. Simulated data were re-estimated using the MTP–GPDI model implemented in Bliss Independence or Loewe Additivity, or using a conventional model such as an Empirical Bliss Independence-based model or the Greco model based on Loewe Additivity. The GPDI model correctly characterized different PD interactions (antagonism, synergism, or asymmetric interaction), regardless of the underlying additivity criterion. The commonly used conventional models were not able to characterize asymmetric PD interactions, i.e., concentration-dependent synergism and antagonism. An optimized experimental design was developed that correctly identified interactions in ≥ 94% of the evaluated scenarios using the MTP–GPDI model approach. The MTP–GPDI model approach was proved to provide advantages to other conventional models for assessing PD interactions of anti-tubercular drugs and provides key information for selection of drug combinations for Phase 2b evaluation.

This study was carried out to understand the influence of a selected antiarrhythmic drug on the pharmacodynamics and pharmacokinetics of an antidiabetic drug in animal models. Pharmacodynamic and pharmacokinetic responses were determined by measurements of blood glucose and serum insulin and serum metformin to drug interactions between disopyramide and metformin. Single dose and multi dose studies showed that the maximum blood glucose reductions in normal and diabetic rats were at the 6th hour, and in rabbits at the 3rd hour. Glucose-insulin homeostasis was evaluated to assess the safety and effectiveness of the combination. There was a marginal increase in the pharmacokinetic parameters of metformin with multiple dose treatments of disopyramide but no significant changes in kinetic parameters between single and multiple dose studies, compared to metformine alone. There may be a possibility of disopyramide and metformin interaction at the excretion stage, or an additive pharmacodynamic action. This study validates the drug interaction in two dissimilar species, which indicates more probability of its occurrence in humans.

Inferring drug–drug interactions (DDIs) is an essential step in drug development and drug administration. Most computational inference methods focus on modeling drug pharmacokinetics, aiming at interactions that result from a common metabolizing enzyme (CYP). Here, we introduce a novel prediction method, INDI (INferring Drug Interactions), allowing the inference of both pharmacokinetic, CYP‐related DDIs (along with their associated CYPs) and pharmacodynamic, non‐CYP associated ones. On cross validation, it obtains high specificity and sensitivity levels (AUC (area under the receiver‐operating characteristic curve)⩾0.93). In application to the FDA adverse event reporting system, 53% of the drug events could potentially be connected to known (41%) or predicted (12%) DDIs. Additionally, INDI predicts the severity level of each DDI upon co‐administration of the involved drugs, suggesting that severe interactions are abundant in the clinical practice. Examining regularly taken medications by hospitalized patients, 18% of the patients receive known or predicted severely interacting drugs and are hospitalized more frequently. Access to INDI and its predictions is provided via a web tool at http://www.cs.tau.ac.il/∼bnet/software/INDI , facilitating the inference and exploration of drug interactions and providing important leads for physicians and pharmaceutical companies alike. INDI is a similarity‐based drug–drug interaction prediction method that can infer both pharmacokinetic and pharmacodynamic interactions, as well as their severity levels. Both known and predicted drug interactions are found to be prevalent in clinical practice. Synopsis INDI is a similarity‐based drug–drug interaction prediction method that can infer both pharmacokinetic and pharmacodynamic interactions, as well as their severity levels. Both known and predicted drug interactions are found to be prevalent in clinical practice. INDI is a similarity‐based drug–drug interaction prediction method, capable of handling both pharmacokinetic and pharmacodynamic interactions. INDI predicts the severity of the interaction and the Cytochrome P450 isozyme involved in pharmacokinetic interactions. We show the prevalence of known and predicted drug interactions in drug adverse reports and in chronic medications taken by hospitalized patients.

Pharmacodynamic interactions between plant isolated compounds are important to understand the mode of action of an herbal extract to formulate or create better standardized extracts, phytomedicines, or phytopharmaceuticals. In this work, we propose binary mixtures using a leader compound to found pharmacodynamic interactions in inhibition of the NF-κB/AP-1 pathway using RAW-Blue™ cells. Eight compounds were isolated from Castilleja tenuiflora, four were new furofuran-type lignans for the species magnolin, eudesmin, sesamin, and kobusin. Magnolin (60.97%) was the most effective lignan inhibiting the NF-κB/AP-1 pathway, followed by eudesmin (56.82%), tenuifloroside (52.91%), sesamin (52.63%), and kobusin (45.45%). Verbascoside, a major compound contained in wild C. tenuiflora showed an inhibitory effect on NF-κB/AP-1. This polyphenol was chosen as a leader compound for binary mixtures. Verbacoside-aucubin and verbascoside-kobusin produced synergism, while verbascoside-tenuifloroside had subadditivity in all concentrations. Verbascoside-kobusin is a promising mixture to use on NF-κB/AP-1 related diseases and anti-inflammatory C. tenuiflora-based phytomedicines.

Abstract Background The use of ketamine for depression has increased rapidly in the past decades. Ketamine is often prescribed as an add-on to other drugs used in psychiatric patients, but clear information on drug-drug interactions is lacking. With this review, we aim to provide an overview of the pharmacodynamic interactions between ketamine and mood stabilizers, benzodiazepines, monoamine oxidase-inhibitors, antipsychotics, and psychostimulants. Methods MEDLINE and Web of Science were searched. Results Twenty-four studies were included. For lithium, no significant interactions with ketamine were reported. Two out of 5 studies on lamotrigine indicated that the effects of ketamine were attenuated. Benzodiazepines were repeatedly shown to reduce the duration of ketamine’s antidepressant effect. For the monoamine oxidase-inhibitor tranylcypromine, case reports showed no relevant changes in vital signs during concurrent S-ketamine use. One paper indicated an interaction between ketamine and haloperidol, 2 other studies did not. Four papers investigated risperidone, including 3 neuroimaging studies showing an attenuating effect of risperidone on ketamine-induced brain perfusion changes. Clozapine significantly blunted ketamine-induced positive symptoms in patients with schizophrenia but not in healthy participants. One paper reported no effect of olanzapine on ketamine’s acute psychotomimetic effects. Conclusion Current literature shows that benzodiazepines and probably lamotrigine reduce ketamine’s treatment outcome, which should be taken into account when considering ketamine treatment. There is evidence for an interaction between ketamine and clozapine, haloperidol, and risperidone. Due to small sample sizes, different subject groups and various outcome parameters, the evidence is of low quality. More studies are needed to provide insight into pharmacodynamic interactions with ketamine.

Patients with type 2 diabetes mellitus often require multifactorial pharmacological treatment due to different comorbidities. An increasing number of concomitantly taken medications elevate the risk of the patient experiencing adverse drug effects or drug interactions. Drug interactions can be divided into pharmacokinetic and pharmacodynamic interactions affecting cytochrome (CYP) enzymes, absorption properties, transporter activities and receptor affinities. Furthermore, nutrition, herbal supplements, patient’s age and gender are of clinical importance. Relevant drug interactions are predominantly related to sulfonylureas, thiazolidinediones and glinides. Although metformin has a very low interaction potential, caution is advised when drugs that impair renal function are used concomitantly. With the exception of saxagliptin, dipeptidyl peptidase-4 (DPP-4) inhibitors also show a low interaction potential, but all drugs affecting the drug transporter P-glycoprotein should be used with caution. Incretin mimetics and sodium–glucose cotransporter-2 (SGLT-2) inhibitors comprise a very low interaction potential and are therefore recommended as an ideal combination partner from the clinical–pharmacologic point of view.

Diabetes is a complex condition with a variety of causes and pathophysiologies. The current single target approach has not provided ideal clinical outcomes for the treatment of the disease and its complications. Herbal medicine has been used for the management of various diseases such as diabetes over centuries. Many diabetic patients are known to use herbal medicines with antidiabetic properties in addition to their mainstream treatments, which may present both a benefit as well as potential risk to effective management of their disease. In this review we evaluate the clinical and experimental literature on herb–drug interactions in the treatment of diabetes. Pharmacokinetic and pharmacodynamic interactions between drugs and herbs are discussed, and some commonly used herbs which can interact with antidiabetic drugs summarised. Herb–drug interactions can be a double-edged sword presenting both risks (adverse drug events) and benefits (through enhancement). There is a general lack of data on herb–drug interactions. As such, more rigorous scientific research is urgently needed to guide clinical practice as well as to safeguard the wellbeing of diabetes patients.

Background: Major depressive disorder may be encountered in 17% of patients with epilepsy and in patients with drug-resistant epilepsy its prevalence may reach 30%. This indicates that patients with epilepsy may require antidepressant treatment. Purpose: Both pharmacodynamic and pharmacokinetic interactions between antiepileptic (antiseizure) and antidepressant drugs have been reviewed. Also, data on the adverse effects of co-administration of antiepileptic with antidepressant drugs have been added. This article was submitted to Neuropharmacology, a section of the journal Frontiers in Pharmacology. Methods: The review of relevant literature was confined to English-language publications in PUBMED databases. Table data show effects of antidepressants on the seizure susceptibility in experimental animals, results of pharmacodynamic interactions between antiepileptic and antidepressant drugs mainly derived from electroconvulsions in mice, as well as results concerning pharmacokinetic interactions between these drugs in clinical conditions. Conclusion: Antidepressant drugs may exert differentiated effects upon the convulsive threshold which may differ in their acute and chronic administration. Animal data indicate that chronic administration of antidepressants could reduce (mianserin, trazodone) or potentiate the anticonvulsant activity of some antiepileptics (fluoxetine, reboxetine, venlafaxine). There are also examples of neutral interactions (milnacipran).

Because of the high research and development cost of new drugs, the long development process of new drugs, and the high failure rate at later stages, combining past drugs has gradually become a more economical and attractive alternative. However, the ensuing problem of drug–drug interactions (DDIs) urgently need to be solved, and combination has attracted a lot of attention from pharmaceutical researchers. At present, DDI is often evaluated and investigated from two perspectives: pharmacodynamics and pharmacokinetics. However, in some special cases, DDI cannot be accurately evaluated from a single perspective. Therefore, this review describes and compares the current DDI evaluation methods based on two aspects: pharmacokinetic interaction and pharmacodynamic interaction. The methods summarized in this paper mainly include probe drug cocktail methods, liver microsome and hepatocyte models, static models, physiologically based pharmacokinetic models, machine learning models, in vivo comparative efficacy studies, and in vitro static and dynamic tests. This review aims to serve as a useful guide for interested researchers to promote more scientific accuracy and clinical practical use of DDI studies.