Drug-Drug Interactions and Synergy: From Pharmacological Models to Clinical Application

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts, such as concentration-response curves, additive effects, and predictive models, are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. Although various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors, such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care. SIGNIFICANCE STATEMENT: Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug-drug interactions research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

Assessment of risk factors associated with potential drug-drug interactions among patients suffering from chronic disorders

Patients suffering from chronic diseases are more likely to experience pDDIs due to older age, prolonged treatment, severe illness and greater number of prescribed drugs. The objective of the current study was to assess the prevalence of pDDIs and risk factors associated with occurrence of pDDIs in chronic disease patients attending outpatient clinics for regular check-ups. Patients suffering from diabetes, chronic obstructive pulmonary disease (COPD), stroke and osteoporosis were included in the study. This study was a cross sectional, observational, prospective study that included 337 patients from outpatient clinics of respiratory ward, cardiac ward and orthopedic ward of Nishter Hospital Multan, Pakistan. The mean number of interactions per patient was 1.68. A greater risk for occurrence of pDDI was associated with older age ≥ 60 years (OR = 1.95, 95% CI = 1.44-2.37, p<0.001); polypharmacy (≥ 5 drugs) (OR = 3.74, 95% CI 2.32-4.54, p<0.001); overburden (OR = 2.23, 95% CI = 1.64-3.16, p<0.01); CCI score (OR = 1.28, 95% CI = 1.04-1.84, p<0.001); multiple prescribers to one patient (OR = 1.18, 95% CI = 1.06-1.41, p<0.01); and trainee practitioner (OR = 1.09, 95% CI = 1.01-1.28, p<0.01). Old age, polypharmacy, overburden healthcare system, higher comorbidity index, multiple prescribers to one patient and trainee practitioner were associated with increased risk of occurrence of pDDIs in chronic disease patients.

Man vs. machine: comparison of pharmacogenetic expert counselling with a clinical medication support system in a study with 200 genotyped patients

Background

Medication problems such as strong side effects or inefficacy occur frequently. At our university hospital, a consultation group of specialists takes care of patients suffering from medication problems. Nevertheless, the counselling of poly-treated patients is complex, as it requires the consideration of a large network of interactions between drugs and their targets, their metabolizing enzymes, and their transporters, etc.

Purpose

This study aims to check whether a score-based decision-support system (1) reduces the time and effort and (2) suggests solutions at the same quality level.

Patients and methods

A total of 200 multimorbid, poly-treated patients with medication problems were included. All patients were considered twice: manually, as clinically established, and using the Drug-PIN decision-support system. Besides diagnoses, lab data (kidney, liver), phenotype (age, gender, BMI, habits), and genotype (genetic variants with actionable clinical evidence I or IIa) were considered, to eliminate potentially inappropriate medications and to select individually favourable drugs from existing medication classes. The algorithm is connected to automatically updated knowledge resources to provide reproducible up-to-date decision support.

Results

The average turnaround time for manual poly-therapy counselling per patient ranges from 3 to 6 working hours, while it can be reduced to ten minutes using Drug-PIN. At the same time, the results of the novel computerized approach coincide with the manual approach at a level of > 90%. The holistic medication score can be used to find favourable drugs within a class of drugs and also to judge the severity of medication problems, to identify critical cases early and automatically.

Conclusion

With the computerized version of this approach, it became possible to score all combinations of all alternative drugs from each class of drugs administered (“personalized medication landscape “) and to identify critical patients even before problems are reported (“medication alert”). Careful comparison of manual and score-based results shows that the incomplete manual consideration of genetic specialties and pharmacokinetic conflicts is responsible for most of the (minor) deviations between the two approaches. The meaning of the reduction of working time for experts by about 2 orders of magnitude should not be underestimated, as it enables practical application of personalized medicine in clinical routine.

Structural Modifications Yield Novel Insights Into the Intriguing Pharmacodynamic Potential of Anti-inflammatory Nitro-Fatty Acids

Endogenous nitro-fatty acids (NFA) are potent electrophilic lipid mediators that exert biological effects in vitro and in vivo via selective covalent modification of thiol-containing target proteins. The cytoprotective, anti-inflammatory, and anti-tumorigenic effects of NFA in animal models of disease caused by targeted protein nitroalkylation are a valuable basis for the development of future anti-phlogistic and anti-neoplastic drugs. Considering the complexity of diseases and accompanying comorbidities there is an urgent need for clinically effective multifunctional drugs. NFA are composed of a fatty acid backbone containing a nitroalkene moiety triggering Michael addition reactions. However, less is known about the target-specific structure–activity relationships and selectivities comparing different NFA targets. Therefore, we analyzed 15 NFA derivatives and compared them with the lead structure 9-nitro-oleic acid (9NOA) in terms of their effect on NF-κB (nuclear factor kappa B) signaling inhibition, induction of Nrf-2 (nuclear factor erythroid 2-related factor 2) gene expression, sEH (soluble epoxide hydrolase), LO (lipoxygenase), and COX-2 (cyclooxygenase-2) inhibition, and their cytotoxic effects on colorectal cancer cells. Minor modifications of the Michael acceptor position and variation of the chain length led to drugs showing increased target preference or enhanced multi-targeting, partly with higher potency than 9NOA. This study is a significant step forward to better understanding the biology of NFA and their enormous potential as scaffolds for designing future anti-inflammatory drugs.

Risk factors for potential drug-drug interactions in patients with myasthenia gravis

OBJECTIVES

Our aim was to determine risk factors for and frequency of potential drug-drug interactions (pDDIs) among hospitalized patients with myasthenia gravis (MG).

METHODS

This was a retrospective cross-sectional study of the-first time hospitalized MG patients or patients hospitalized because of the exacerbation of MG at the Neurology Clinic of the Clinical Center of Serbia, Belgrade. Medical records and discharge summaries of hospitalized MG patients over a 10-year period were reviewed. The pDDIs were identified by means of Micromedex, and multivariate regression methods were used to reveal potential predictors of number of pDDIs per patient.

RESULTS

The study included 687 patients with MG. In total, 2041 pDDIs were detected in 608 (88.5%) patients. Among the discovered pDDIs, 329 different pDDIs were observed. The most frequent pDDIs were pyridostigmine-prednisone (487patients/70.9%) and aspirin-prednisone (90 patients/13.1%) classified as moderate, and enalapril-potassium chloride (71patients/10.3%) classified as major pDDI. Five drugs (aspirin, insulin, prednisone, cyclosporine, metformin) were responsible for 22.6% of different pDDIs. Dyspnea, generalized form of MG, diabetes mellitus, hypertension, total number of drugs-used, use of antiplatelets were identified as the relevant risk factors for total number of pDDIs (R2 = 0.626,F = 73.797, p < 0.001), while age of patients and history of cancer were inversely correlated with such an outcome.

CONCLUSION

The frequency of the pDDIs in hospitalized MG patients is high, and adversely influenced by dyspnea, generalized MG, diabetes mellitus, hypertension, total number of drugs-used and use of antiplatelets.

Risk Factors Associated With Medication Errors Among Patients Suffering From Chronic Disorders

Introduction: Medication error is unintentional and can be reduced by reducing the risk factors. Patients suffering from chronic diseases are at an increased risk of medication errors.

Objective: This work aims to assess the risk factors associated with medication errors among patients suffering from chronic disorders in hospitals of South Punjab, Pakistan.

Methodology: Multiple logistic regression analysis was used to assess the impact of different risk factors on the prevalence of medication errors in patients suffering from chronic diseases.

Results: A greater risk for the occurrence of medication errors was associated with age ≥60 years (odds ratio, OR = 1.9; 95% CI = 1.3–3.1; p = 0.001), overburdened healthcare system (OR = 2.2; 95% CI = 1.64–3.56; p < 0.000), number of prescribed drugs ≥5 (OR = 1.74; 95% CI = 1.02–2.64; p < 0.000), comorbidities (OR = 2.6; 95% CI = 1.72–3.6; p = 0.003), Charlson comorbidity index (OR = 1.31; 95% CI = 0.49–1.84; p = 0.004), and multiple prescribers to one patient (OR = 1.12; 95% CI = 0.64–1.76; p = 0.001).

Conclusion: Older age, overburdened healthcare system, number of prescribed drugs, comorbidities, Charlson comorbidity index, and multiple prescribers to one patient are significant risk factors for the occurrence of medication errors.

Potential Drug-drug Interactions in Acute Ischemic Stroke Patients at the Neurological Intensive Care Unit

Background

Clinically relevant potential drug-drug interactions are considered preventable adverse drug reactions.

Objective

The aim of this study was to ascertain the frequency of potential drug-drug interactions in acute ischemic stroke patients and to explore factors associated with occurrence of potentially contraindicated drug-drug interactions.

Methods

This observational retrospective cohort and nested case-control study was carried out among patients treated for acute ischemic stroke at the Neurological Intensive Care Unit in the Clinical Centre Kragujevac, Serbia. The potentially drug-drug interactions for each day of hospitalization were identifi ed using Micromedex® soft ware. Based on the existence or absence of potentially contraindicated drug-drug interactions, the participants were divided into a group of cases (n=111) and the control group (n=444).

Results

A total of 696 patients were analysed. All patients had a minimum of one potential drug-drug interaction during hospitalization. The most common drugs involved in potential drug-drug interactions were aspirin (8.02%), diclofenac (7.49%) and warfarin (7.14%). The number of medications prescribed for simultaneous use during hospitalisation and the use of antipsychotics in therapy signifi cantly increased the likelihood of potentially contraindicated drug-drug interactions aft er adjustment by means of logistic regression for 1.2 and 3 times, respectively.

Conclusions

This study suggests that patients with acute ischemic stroke are frequently exposed to potential drug-drug interactions. It is essential to identify potentially drug-drug interactions in these patients as early as possible in order to prevent adverse drug reactions and ensure safe recovery. Besides, full attention should be paid when adding each new medication in therapy, particularly when a neurologist decides to prescribe antipsychotics, such as risperidone.

Drug interactions among older adults followed up in a comprehensive medication management service at Primary Care

Objective:

To estimate the prevalence of drug interactions and associated factors among older adults followed up in a Comprehensive Medication Management Service at Primary Care.

Methods:

Firstly, the Beers criteria 2015 was used to define drug interactions; later, drug interactions proposed by Dumbreck for patients with diabetes, depression, and heart failure were evaluated. The associated factors were assessed by univariate (Pearson's χ²) and multivariate analyses (logistic regression). The significance level of 5% was set for all analyses.

Results:

The mean age of the studied population was 70.2±7.8 years; 52.2% were between 60 and 69 years, and 61.3% were female. Among the older adults, 94.5% used two or more drugs (condition for the occurrence of drug-drug interaction). The prevalence of drug interaction according to the Beers criteria was 4.9%. After multivariate analysis, diseases of the central nervous system, arrhythmia, number of medications, and female sex were positively associated with drug interaction. The prevalence of drug interaction according to Dumbreck was 27.2%. After multivariate analysis, the number of medications, the presence of heart failure, and Charlson comorbidity index greater than 1 were conditions positively associated with drug interactions.

Conclusion:

The holistic and individualized approach used in comprehensive medication management services for older patients is important, considering the prevalence of drug interactions and the need to minimize adverse events.

Drug-drug interactions and pharmacists' interventions among psychiatric patients in outpatient clinics of a teaching hospital in Saudi Arabia

Background

Lack of recognition of labeled drug-drug interactions (DDIs) is a type of medication error of particular relevance to the treatment of psychiatric patients. Pharmacists are in a position to detect and address potential DDIs.

Objective

This study aimed to explore pharmacists' role in the identification and management of DDIs among psychiatric patients in psychiatric outpatient clinics of a university-affiliated tertiary care hospital in Riyadh, Saudi Arabia.

Method

This study was a retrospective, cross-sectional medical chart review of patients visiting outpatient psychiatric clinics. It utilized medical records of patients who were taking any psychotropic medications and were prescribed at least one additional drug. The hospital Computerized Physician Order Entry system was used to identify DDIs and determine the pharmacists' interventions. The Beers criteria were applied to detect inappropriate prescribing among older patients.

Results

On average, the pharmacists intervened in 12 out of 213 (5.6%) cases of major or moderate DDIs. Older age, higher number of prescription medications, the severity of DDIs, and the utilization of lithium and anticoagulants were positively associated with the pharmacist undertaking an action.

Conclusion

Future studies should explore the prevalence rate of harmful DDIs among psychiatric patients on a large scale and examine the effectiveness of different pharmacy policies in the detection and management of DDIs.