Search results for: drug interactions

Authors: Sara Ataei, Molouk Hadjibabaie, Shirinsadat Badri, Amirhossein Moslehi, Iman Karimzadeh, Ardeshir Ghavamzadeh

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Purpose: To assess the pattern and probable risk factors for moderate and major drug–drug interactions in a referral hematology-oncology ward in Iran. Methods: All patients admitted to hematology–oncology ward of Dr. Shariati Hospital during a 6-month period and received at least two anti-cancer or non-anti-cancer medications simultaneously were included. All being scheduled anti-cancer and non-anti-cancer medications both prescribed and administered during ward stay were considered for drug–drug interaction screening by Lexi-Interact On- Desktop software. Results: One hundred and eighty-five drug–drug interactions with moderate or major severity were detected from 83 patients. Most of drug–drug interactions (69.73 %) were classified as pharmacokinetics. Fluconazole (25.95 %) was the most commonly offending medication in drug–drug interactions. Interaction of sulfamethoxazole-trimethoprim with fluconazole was the most common drug–drug interaction (27.27 %). Vincristine with imatinib was the only identified interaction between two anti-cancer agents. The number of administered medications during ward stay was considered as an independent risk factor for developing a drug–drug interaction. Conclusions: Potential moderate or major drug–drug interactions occur frequently in patients with hematological malignancies or related diseases. Performing larger standard studies are required to assess the real clinical and economical effects of drug–drug interactions on patients with hematological and non-hematological malignancies.

Keywords: drug–drug interactions, hematology–oncology ward, hematological malignancies

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Mustafa Malki, Ewan R. Pearson

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Tools utilized by health care practitioners to flag potential adverse drug reactions secondary to drug-drug interactions ignore individual genetic variation, which has the potential to markedly alter the severity of these interactions. To our best knowledge, there have been limited published studies on the impact of genetic variation on drug-drug interactions. Therefore, our aim in this project is the discovery of previously unrecognized, clinically important drug-drug-gene interactions (DDGIs) within the list of most commonly used drug combinations in the UK. The UKBB database was utilized to identify the top most frequently prescribed drug combinations in the UK with at least one route of interaction (over than 200 combinations were identified). We have recognised 37 common and unique interacting genes considering all of our drug combinations. Out of around 600 potential genetic variants found in these 37 genes, 100 variants have met the selection criteria (common variant with minor allele frequency ≥ 5%, independence, and has passed HWE test). The association between these variants and the use of each of our top drug combinations has been tested with a case-control analysis under the log-additive model. As the data is cross-sectional, drug intolerance has been identified from the genotype distribution as presented by the lower percentage of patients carrying the risky allele and on the drug combination compared to those free of these risk factors and vice versa with drug tolerance. In GoDARTs database, the same list of common drug combinations identified by the UKBB was utilized here with the same list of candidate genetic variants but with the addition of 14 new SNPs so that we have a total of 114 variants which have met the selection criteria in GoDARTs. From the list of the top 200 drug combinations, we have selected 28 combinations where the two drugs in each combination are known to be used chronically. For each of our 28 combinations, three drug response phenotypes have been identified (drug stop/switch, dose decrease, or dose increase of any of the two drugs during their interaction). The association between each of the three phenotypes belonging to each of our 28 drug combinations has been tested against our 114 candidate genetic variants. The results show replication of four findings between both databases : (1) Omeprazole +Amitriptyline +rs2246709 (A > G) variant in CYP3A4 gene (p-values and ORs with the UKBB and GoDARTs respectively = 0.048,0.037,0.92,and 0.52 (dose increase phenotype)) (2) Simvastatin + Ranitidine + rs9332197 (T > C) variant in CYP2C9 gene (0.024,0.032,0.81, and 5.75 (drug stop/switch phenotype)) (3) Atorvastatin + Doxazosin + rs9282564 (T > C) variant in ABCB1 gene (0.0015,0.0095,1.58,and 3.14 (drug stop/switch phenotype)) (4) Simvastatin + Nifedipine + rs2257401 (C > G) variant in CYP3A7 gene (0.025,0.019,0.77,and 0.30 (drug stop/switch phenotype)). In addition, some other non-replicated, but interesting, significant findings were detected. Our work also provides a great source of information for researchers interested in DD, DG, or DDG interactions studies as it has highlighted the top common drug combinations in the UK with recognizing 114 significant genetic variants related to drugs' pharmacokinetic.

Keywords: adverse drug reactions, common drug combinations, drug-drug-gene interactions, pharmacogenomics

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Authors: Jay Ananth

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The drug discovery process currently requires numerous years of clinical testing as well as money just for a single drug to earn FDA approval. For drugs that even make it this far in the process, there is a very slim chance of receiving FDA approval, resulting in detrimental hurdles to drug accessibility. To minimize these inefficiencies, numerous studies have implemented computational methods, although few computational investigations have focused on a crucial feature of drugs: lipophilicity. Lipophilicity is a physical attribute of a compound that measures its solubility in lipids and is a determinant of drug efficacy. This project leverages Artificial Intelligence to predict the impact of a drug’s lipophilicity on its performance by accounting for factors such as binding affinity and toxicity. The model predicted lipophilicity and binding affinity in the validation set with very high R² scores of 0.921 and 0.788, respectively, while also being applicable to a variety of target receptors. The results expressed a strong positive correlation between lipophilicity and both binding affinity and toxicity. The model helps in both drug development and discovery, providing every pharmaceutical company with recommended lipophilicity levels for drug candidates as well as a rapid assessment of early-stage drugs prior to any testing, eliminating significant amounts of time and resources currently restricting drug accessibility.

Keywords: drug discovery, lipophilicity, ligand-receptor interactions, machine learning, drug development

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Authors: Asfa Hussain, Chee Tang, Mien Nguyen

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Introduction: The safe usage of medications is dependent on clear, well-documented prescribing. Medical drug charts should be regularly checked to ensure that they are fit for purpose. Aims: The purpose of this study was to evaluate whether the Isabel Hospice drug charts were effective or prone to medical errors. The aim was to create a comprehensive palliative care drug chart in line with medico-legal guidelines and to minimise drug administration and prescription errors. Methodology: 50 medical drug charts were audited from March to April 2020, to assess whether they complied with medico-legal guidelines, in a hospice within East of England. Meetings were held with the larger multi-disciplinary team (MDT), including the pharmacists, nursing staff and doctors, to raise awareness of the issue. A preliminary drug chart was created, using the input from the wider MDT. The chart was revised and trialled over 15 times, and each time feedback from the MDT was incorporated into the subsequent template. In the midst of the COVID-19 pandemic in September 2020, the finalised drug chart was trialled. 50 new palliative drug charts were re-audited, to evaluate the changes made. Results: Prescribing and administration errors were high prior to the implementation of the new chart. This improved significantly after introducing the new drug charts, therefore improving patient safety and care. The percentage of inadequately documented allergies went down from 66% to 20% and incorrect oxygen prescription from 40% to 16%. The prescription drug-drug interactions decreased by 30%. Conclusion: It is vital to have clear standardised drug charts, in line with medico-legal standards, to allow ease of prescription and administration of medications and ensure optimum patient-centred care. This closed loop audit demonstrated significant improvement in documentation and prevention of possible fatal drug errors and interactions.

Keywords: palliative care, drug chart, medication errors, drug-drug interactions, COVID-19, patient safety

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Authors: Elena K. Schneider, Johnny X. Huang, Vincenzo Carbone, Mark Baker, Mohammad A. K. Azad, Matthew A. Cooper, Jian Li, Tony Velkov

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Ivacaftor is a novel CF trans-membrane conductance regulator (CFTR) potentiator that improves the pulmonary function for cystic fibrosis patients bearing a G551D CFTR-protein mutation. Because ivacaftor is highly bound (>97%) to plasma proteins, there is the strong possibility that co-administered CF drugs that compete for the same plasma protein binding sites and impact the free drug concentration. This in turn could lead to drastic changes in the in vivo efficacy of ivacaftor and therapeutic outcomes. This study compares the binding affinity of ivacaftor and co-administered CF drugs for human serum albumin (HSA) and α1-acid glycoprotein (AGP) using surface plasmon resonance and fluorimetric binding assays that measure the displacement of site selective probes. Due to their high plasma protein binding affinities, drug-drug interactions between ivacaftor are to be expected with ducosate, montelukast, ibuprofen, dicloxacillin, omeprazole and loratadine. The significance of these drug-drug interactions is interpreted in terms of the pharmacodynamic/pharmacokinetic parameters and molecular docking simulations. The translational outcomes of the data are presented as recommendations for a staggered treatment regimen for future clinical trials which aims to maximize the effective free drug concentration and clinical efficacy of ivacaftor.

Keywords: human α-1-acid glycoprotein, binding affinity, human serum albumin, ivacaftor, cystic fibrosis

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Authors: Ephrem Mebratu Dagnew, Mohammed Biset Ayalew, Gizework Alemnew Mekonnen, Alehegn Bishaw Geremew, Ousman Abubeker Abdela

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Objective: To assess the prevalence of drug-related problems and associated factors among adult psychiatric inpatients. Method: Hospital-based multicenter cross-sectional observational study was conducted from April to July 2021 at five randomly selected hospitals in North-west Ethiopia. A total of 325 consecutively sampled patients participated in the study. Clinical pharmacists assessed the DRPs based on clinical judgment supported by updated evidence-based diseases guidelines. A Medscape drug-interactions checker was used to check drug-drug interactions. The results were summarized using descriptive statistics, including frequency, mean, and standard deviation. Odds ratio (OR) with 95% confidence interval were also computed for each variable for the corresponding P-value. The value of P ≤ 0.05 was considered statistically significant. Result : From the total of 325 study participants, more than half of them (52.9%) were females and the mean age ± (standard deviation) was 30.8±11.3 years. At least one drug-related problem was recorded from 60.9%, 95% CI (55.7-65.8) of study participants with a mean of 0.6±0.49 per patient. Need additional drug therapy was the most common DRP (22.8%), followed by non-adherence to medicine (20.6%) and adverse drug reactions (11%), respectively. Factors independently associated with drug-related problems were rural residence [AOR=1.96,95%CI:1.01-2.84, P-value=0.046], self-employed [AOR=6.0 ,95% CI: 1.0-36.9, P-value=0.035] and alcohol drinkers [AOR=6.40,95%CI:1.12-37.5, p-value=0.034]. Conclusion: The prevalence of drug-related problems among adult psychiatric patients admitted to psychiatric wards was high. Healthcare providers give more attention to tackling these problems. Being a rural residence, self-employed, and Alcohol drinkers were associated with drug-related problems.

Keywords: psychiatric patients, drug-relatedproblems, multicenter, Ethiopia

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Authors: Reza Ferdousi, Reza Safdari, Yadollah Omidi

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Drug-drug interactions (DDIs) are main cause of the adverse drug reactions and nature of the functional and molecular complexity of drugs behavior in human body make them hard to prevent and treat. With the aid of new technologies derived from mathematical and computational science the DDIs problems can be addressed with minimum cost and efforts. Market basket analysis is known as powerful method to identify co-occurrence of thing to discover patterns and frequency of the elements. In this research, we used market basket analysis to identify important bio-elements in DDIs occurrence. For this, we collected all known DDIs from DrugBank. The obtained data were analyzed by market basket analysis method. We investigated all drug-enzyme, drug-carrier, drug-transporter and drug-target associations. To determine the importance of the extracted bio-elements, extracted rules were evaluated in terms of confidence and support. Market basket analysis of the over 45,000 known DDIs reveals more than 300 important rules that can be used to identify DDIs, CYP 450 family were the most frequent shared bio-elements. We applied extracted rules over 2,000,000 unknown drug pairs that lead to discovery of more than 200,000 potential DDIs. Analysis of the underlying reason behind the DDI phenomena can help to predict and prevent DDI occurrence. Ranking of the extracted rules based on strangeness of them can be a supportive tool to predict the outcome of an unknown DDI.

Keywords: drug-drug interaction, market basket analysis, rule discovery, important bio-elements

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Authors: G. Agrawal, R. Agrawal, A. Pich

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The pH and temperature responsive biodegradable poly(N-vinylcaprolactam) (PVCL) based microgels functionalized with itaconic acid (IA) units are prepared via precipitation polymerization for drug delivery applications. Volume phase transition temperature (VPTT) of the obtained microgels is influenced by both IA content and pH of the surrounding medium. The developed microgels can be degraded under acidic conditions due to the presence of hydrazone based crosslinking points inside the microgel network. The microgel particles are able to effectively encapsulate doxorubicin (DOX) drug and exhibit low drug leakage under physiological conditions. At low pH, rapid DOX release is observed due to the changes in electrostatic interactions along with the degradation of particles. The results of the cytotoxicity assay further display that the DOX-loaded microgel exhibit effective antitumor activity against HeLa cells demonstrating their great potential as drug delivery carriers for cancer therapy.

Keywords: degradable, drug delivery, hydrazone linkages, microgels, responsive

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Authors: Yasin I. Tayem, Jamil Ahmed, Mahmood Bahzad, Abdullah Alnama, Fahad Al Asfoor, Mahmood A. Jalil, Mohammed Radhi, Ahmed Alenezi, Khalid A. J. Al-Khaja

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Drug-drug interactions (DDIs) represent a significant cause of patient’s morbidity and mortality. The rate of DDIs is rapidly increasing worldwide with the increasing proportion of ageing population and frequent requirement of polypharmacy-prescription of multiple drugs to treat comorbidities. Prescribing physicians are responsible for checking their prescriptions for the presence and severity of DDIs. However, since a large number of new drugs are approved and marketed every year, new interactions between medications are increasingly reported. Consequently, it is no longer practical for physicians to rely only upon their previous knowledge of medicine to avoid potential DDIs. The aim of this study was to explore the perceptions of physicians working at primary healthcare centers in Bahrain towards DDIs and how they manage them during their practice. Methodology: In this cross-sectional study, physicians working at all governmental primary healthcare centers in Bahrain were invited to voluntarily, privately and anonymously respond to a self-administered questionnaire. The questionnaire aims to assess their self-reported knowledge of DDIs and how they check for them in their practice. The participants were requested to provide socio demographic data and information related to their attitudes towards DDIs including strategies they employ for detecting and managing them, and their awareness of drugs which commonly cause DDIs. At the end of the questionnaire, an open-ended item was added to allow participants to further add any comment. Findings and Conclusions: The study is going on currently, and the results and conclusions will be presented at the conference.

Keywords: awareness, drug interactions, health centres, physicians

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Authors: Leila Baghaarabani, Parvin Razzaghi, Mennatolla Magdy Mostafa, Ahmad Albaqsami, Al Warith Al Rushaidi, Masoud Al Rawahi

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Predicting the interaction between drugs and their molecular targets is pivotal for advancing drug development processes. Due to the time and cost limitations, computational approaches have emerged as an effective approach to drug-target interaction (DTI) prediction. Most of the introduced computational based approaches utilize the drug molecule and protein sequence as input. This study does not only utilize these inputs, it also introduces a protein representation developed using a masked protein language model. In this representation, for every individual amino acid residue within the protein sequence, there exists a corresponding probability distribution that indicates the likelihood of each amino acid being present at that particular position. Then, the similarity between each pair of amino-acids is computed to create similarity matrix. To encode the knowledge of the similarity matrix, Bi-Level Routing Attention (BiFormer) is utilized, which combines aspects of transformer-based models with protein sequence analysis and represents a significant advancement in the field of drug-protein interaction prediction. BiFormer has the ability to pinpoint the most effective regions of the protein sequence that are responsible for facilitating interactions between the protein and drugs, thereby enhancing the understanding of these critical interactions. Thus, it appears promising in its ability to capture the local structural relationship of the proteins by enhancing the understanding of how it contributes to drug protein interactions, thereby facilitating more accurate predictions. To evaluate the proposed method, it was tested on two widely recognized datasets: Davis and KIBA. A comprehensive series of experiments was conducted to illustrate its effectiveness in comparison to cuttingedge techniques.

Keywords: BiFormer, transformer, protein language processing, self-attention mechanism, binding affinity, drug target interaction, similarity matrix, protein masked representation, protein language model

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Authors: Lynn Lehmann, Dinorah Leyva, Ana Lazic, Stefan Duhr, Philipp Baaske

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Characterization of biomolecular interactions, such as protein-protein, protein-nucleic acid or protein-small molecule, provides critical insights into cellular processes and is essential for the development of drug diagnostics and therapeutics. Here we present a novel, label-free, and tether-free technology to analyze picomolar to millimolar affinities of biomolecular interactions by Microscale Thermophoresis (MST). The entropy of the hydration shell surrounding molecules determines thermophoretic movement. MST exploits this principle by measuring interactions using optically generated temperature gradients. MST detects changes in the size, charge and hydration shell of molecules and measures biomolecule interactions under close-to-native conditions: immobilization-free and in bioliquids of choice, including cell lysates and blood serum. Thus, MST measures interactions under close-to-native conditions, and without laborious sample purification. We demonstrate how MST determines the picomolar affinities of antibody::antigen interactions, and protein::protein interactions measured from directly from cell lysates. MST assays are highly adaptable to fit to the diverse requirements of different and complex biomolecules. NanoTemper´s unique technology is ideal for studies requiring flexibility and sensitivity at the experimental scale, making MST suitable for basic research investigations and pharmaceutical applications.

Keywords: biochemistry, biophysics, molecular interactions, quantitative techniques

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Authors: Meltem Haktaniyan, Eda Cagli, Irem Erel Goktepe

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Polymers that change their properties in response to different stimuli (e.g. light, temperature, pH, ionic strength or magnetic field) are called ‘smart’ or ‘stimuli-responsive polymers’. These polymers have been widely used in biomedical applications such as sensors, gene delivery, drug delivery or tissue engineering. Temperature-responsive polymers have been studied extensively for controlled drug delivery applications. As regard of pseudo-peptides, poly (2-alky-2-oxazoline)s are considered as good candidates for delivery systems due to their stealth behavior and nontoxicity. In order to build responsive multilayer films for controlled drug release applications from surface, Layer by layer technique (LBL) is a powerful technique with an advantage of nanometer scale control over spatial architecture and morphology. Multilayers can be constructed on surface where non-covalent interactions including electrostatic interactions, hydrogen bonding, and charge-transfer or hydrophobic-hydrophobic interactions. In the present study, hydrogen bounded multilayer films of poly (2-alky-2-oxazoline) s with tannic acid were prepared in order to use as a platform to release Doxorubicin (DOX) from surface with pH and thermal triggers. For this purpose, poly (2-isopropyl-2-oxazoline) (PIPOX) and poly (2-ethyl-2-oxazoline) (PETOX) were synthesized via cationic ring opening polymerization (CROP) with hydroxyl end groups. Two polymeric multilayer systems ((PETOX)/(DOX)-(TA) complexes and (PIPOX)/(DOX)-(TA) complexes) were designed to investigate of controlled release of Doxorubicin (DOX) from surface with pH and thermal triggers. The drug release profiles from the multilayer thin films with alterations of pH and temperature will been examined with UV-Vis Spectroscopy and Fluorescence Spectroscopy.

Keywords: temperature responsive polymers, h-bonded multilayer films, drug release, polyoxazoline

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Authors: Maki Mizogami, Hironori Tsuchiya, Yoshiroh Hayabuchi, Kenji Shigemi

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Since drugs exhibit significant structure-dependent differences in activity and toxicity, their differentiation based on the mechanism of action should have implications for comparative drug efficacy and safety. We aimed to differentiate drug stereoisomers by their stereostructure-selective membrane interactions underlying pharmacological and toxicological effects. Biomimetic lipid bilayer membranes were prepared with phospholipids and sterols (either cholesterol or epicholesterol) to mimic the lipid compositions of neuronal and cardiomyocyte membranes and to provide these membranes with the chirality. The membrane preparations were treated with different classes of stereoisomers at clinically- and pharmacologically-relevant concentrations (25-200 μM), followed by measuring fluorescence polarization to determine the membrane interactivity of drugs to change the physicochemical property of membranes. All the tested drugs acted on lipid bilayers to increase or decrease the membrane fluidity. Drug stereoisomers could not be differentiated when interacting with the membranes consisting of phospholipids alone. However, they stereostructure-selectively interacted with neuro-mimetic and cardio-mimetic membranes containing 40 mol% cholesterol ((3β)-cholest-5-en-3-ol) to show the relative potencies being local anesthetic R(+)-bupivacaine > rac-bupivacaine > S(‒)-bupivacaine, α2-adrenergic agonistic D-medetomidine > rac-medetomidine > L-medetomidine, β-adrenergic antagonistic R(+)-propranolol > rac-propranolol > S(–)-propranolol, NMDA receptor antagonistic S(+)-ketamine > rac-ketamine, analgesic monoterpenoid (+)-menthol > (‒)-menthol, non-steroidal anti-inflammatory S(+)-ibuprofen > rac-ibuprofen > R(‒)-ibuprofen, and bioactive flavonoid (+)-epicatechin > (‒)-epicatechin. All of the order of membrane interactivity were correlated to those of beneficial and adverse effects of the tested stereoisomers. In contrast, the membranes prepared with epicholesterol ((3α)-chotest-5-en-3-ol), an epimeric form of cholesterol, reversed the rank order of membrane interactivity to be S(‒)-enantiomeric > racemic > R(+)-enantiomeric bupivacaine, L-enantiomeric > racemic > D-enantiomeric medetomidine, S(–)-enantiomeric > racemic > R(+)-enantiomeric propranolol, racemic > S(+)-enantiomeric ketamine, (‒)-enantiomeric > (+)-enantiomeric menthol, R(‒)-enantiomeric > racemic > S(+)-enantiomeric ibuprofen, and (‒)-enantiomeric > (+)-enantiomeric epicatechin. The opposite configuration allows drug molecules to interact with chiral sterol membranes enantiomer-selectively. From the comparative results, it is speculated that a 3β-hydroxyl group in cholesterol is responsible for the enantioselective interactions of drugs. In conclusion, the differentiation of drug stereoisomers by their stereostructure-selective membrane interactions would be useful for designing and predicting drugs with higher activity and/or lower toxicity.

Keywords: chiral membrane, differentiation, drug stereoisomer, enantioselective membrane interaction

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Authors: Bollu Mounica

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Background: Nowadays drug related problems were seen very commonly within the health care practice. These could result in the medication errors, adverse events, drug interactions and harm to patients. Pharmacist has an identified role in minimizing and preventing such type of problems. Objectives: To detect the incidence of drug related problems for the hospitalized patient, and to analyze the clinical pharmacist interventions performed during the review of prescription orders of the general medicine, psychiatry, surgery, pediatrics, gynaecology units of a large tertiary care teaching hospital. Methods: It was a retrospective, observational and interventional study. The analysis took place daily with the following parameters: dose, rate of administration, presentation and/or dosage form, presence of inappropriate/unnecessary drugs, necessity of additional medication, more proper alternative therapies, presence of relevant drug interactions, inconsistencies in prescription orders, physical-chemical incompatibilities/solution stability. From this evaluation, the drug therapy problems were classified, as well as the resulting clinical interventions. For a period starting November 2012 until December 2014, the inpatient medication charts and orders were identified and rectified by ward and practicing clinical pharmacists within the inpatient pharmacy services in a tertiary care teaching hospital on routine daily activities. Data was collected and evaluated. The causes of this problem were identified. Results: A total of 360 patients were followed. Male (71.66%) predominance was noted over females (28.33%). Drug related problems were more commonly seen in patients aged in between 31-60. Most of the DRP observed in the study resulted from the dispensing errors (26.11%), improper drug selection (17.22%), followed by untreated indications (14.4%) Majority of the clinical pharmacist recommendations were on need for proper dispensing (26.11%), and drug change (18.05%). Minor significance of DRPs were noted high (41.11 %), whereas (35.27 %) were moderate and (23.61 %) were major. The acceptance rate of intervening clinical pharmacist recommendation and change in drug therapy was found to be high (86.66%). Conclusion: Our study showed that the prescriptions reviewed had some drug therapy problem and the pharmacist interventions have promoted positive changes needed in the prescriptions. In this context, routine participation of clinical pharmacists in clinical medical rounds facilitates the identification of DRPs and may prevent their occurrence.

Keywords: drug related problems, clinical pharmacist, drug prescriptions, drug related problems, intervention

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Authors: N. Amraoui, D. Hammoutene

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We have performed a theoretical study using dispersion-corrected Density Functional Methods to evaluate a variety of artificial nucleobases as candidates for metal-mediated Hoogsteen-type base pairs. We focus on A-M-T Hoogsteen-type base pair with M=Co(II), Ru(I), Ni(I). All calculations are performed using (ADF 09) program. Metal-mediated Hoogsteen-type base pairs are studied as drug candidates, their geometry optimizations are performed at ZORA/TZ2P/BLYP-D level. The molecular geometries and different energies as total energies, coordination energies, Pauli interactions, orbital interactions and electrostatic energies are determined.

Keywords: chemistry, biology, density functional method, orbital interactions

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Authors: Ikram Elsiddig, Yacouba Djamila, Amna Hamad

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Abstract—The worldwide increasing of using herbs in form of medicine with or without prescription medications potentiates the interactions between herbal products and conventional medicines; due to more research for herb-drug interactions are needed. for a long time hyperglycemia had been treated with several medicinal plants. A. sativum, belonging to the Liliaceae family is well known for its medicinal uses in African traditional medicine, it used for treating of many human diseases mainly diabetes, high cholesterol and high blood pressure. The purpose of this study is to determine the interaction effect between A. sativum bulb extracts and metformin drug used in diabetes treatment. The in vitro and in vivo evaluation were conducted by glucose reuptake using isolated rats hemidiaphgrams tissue and by estimate glucose tolerance in glucose-loaded wistar albino rats. The results showed that, petroleum ether, chloroform and ethyl acetate extracts were found to have activity of glucose uptake in isolated rats hemidiaphgrams of 24.11 mg/g, 19.07 mg/g and 15.66 mg/g compared to metformin drug of 17 mg/g. These activity were reducded to 17.8 mg/g, 13.59 mg/g and 14.46 mg/g after combination with metformin, metformin itself reduced to 13.59 mg/g, 14.46 mg/g and 12.71 mg/g in comination with chloroform and ethyl acetate. These decrease in activity could be due to herbal–drug interaction between the extracts of A. sativum bulb and metformin drug. The interaction between A. sativum extract and metformin was also shown by in vivo study on the induced hyperglycemic rats. The glucose level after administered of 200 mg/kg was found to be increase with 47.2 % and 17.7% at first and second hour compared to the increase of blood glucose in the control group of 82.6% and76.7%.. At fourth hour the glucose level was became less than normal with 3.4% compared to control which continue to increase with 68.2%. Dose of 400 mg/kg at first hour showed increase in blood glucose of 31.5 %, at second and fourth hours the glucose level was became less than normal with decrease of 3.2 % and 30.4%. After combination the activity was found to be less than that of extract at both high and low dose, whereas, at first and second hour, the glucose level was found to be increase with 50.4% and 21.2%, at fourth hour the glucose level was became less than normal with 14%. Therefore A. sativum could be a potential source for anti-diabetic when it used alone, and it is significant important to use the garlic extract alone instead of combined with Metformin drug in diabetes- treatment.

Keywords: Antagonistic, Garlic, Metformin, Synergistic

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Authors: Leena-Maria Alyedreessy

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Self-esteem is thought to be determined by ones’ own feeling of being included, liked and accepted by others. This research explores whether this concept may also be applied in the virtual world and assesses whether there is any relationship between Twitter users' self-esteem and the amount of interactions they receive. 20 female Arab participants were given a survey asking them about their Twitter interactions and their feelings of having an imagined audience to fill out and a Rosenberg Self-Esteem Assessment to complete. After completion and statistical analysis, results showed a significant correlation between the feeling of being Twitter elite, the feeling of having a lot of people listening to your tweets and having a lot of interactions with high self-esteem. However, no correlations were detected for low-self-esteem and low interactions.

Keywords: twitter, social media, self-esteem, narcissism, interactions

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Authors: Babak Bahri, Fatemeh Yassaee Meybodi, Changiz Eslahchi

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In the pursuit of effective cancer therapies, the exploration of combinatorial drug regimens is crucial to leverage synergistic interactions between drugs, thereby improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug pairs poses challenges due to the vast combinatorial space and limitations of experimental approaches. This study introduces ClusterSyn, a machine learning (ML)-powered framework for classifying anti-cancer drug synergy scores. ClusterSyn employs a two-step approach involving drug clustering and synergy score prediction using a fully connected deep neural network. For each cell line in the training dataset, a drug graph is constructed, with nodes representing drugs and edge weights denoting synergy scores between drug pairs. Drugs are clustered using the Markov clustering (MCL) algorithm, and vectors representing the similarity of drug pairs to each cluster are input into the deep neural network for synergy score prediction (synergy or antagonism). Clustering results demonstrate effective grouping of drugs based on synergy scores, aligning similar synergy profiles. Subsequently, neural network predictions and synergy scores of the two drugs on others within their clusters are used to predict the synergy score of the considered drug pair. This approach facilitates comparative analysis with clustering and regression-based methods, revealing the superior performance of ClusterSyn over state-of-the-art methods like DeepSynergy and DeepDDS on diverse datasets such as Oniel and Almanac. The results highlight the remarkable potential of ClusterSyn as a versatile tool for predicting anti-cancer drug synergy scores.

Keywords: drug synergy, clustering, prediction, machine learning., deep learning

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Authors: Deepika Bhaskar, Neena Wadehra, Megha Gulati, Aruna Narula, R. Vishnu, Gunjan Katyal

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With drug resistance becoming widespread in Plasmodium falciparum infections, development of the alternative drugs is the desired strategy for prevention and cure of malaria. Three drug targets were selected to screen promising drug molecules from the GSK library of around 14000 molecules. Using an in silico structure-based drug designing approach, the differences in binding energies of the substrate and inhibitor were exploited between target sites of parasite and human to design a drug molecule against Plasmodium. The docking studies have shown several promising molecules from GSK library with more effective binding as compared to the already known inhibitors for the drug targets. Though stronger interaction has been shown by several molecules as compare to reference, few molecules have shown the potential as drug candidates though in vitro studies are required to validate the results.

Keywords: plasmodium, malaria, drug targets, in silico studies

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Authors: Alok Shiomurti Tripathi, Ajay Kumar Timiri, Papiya Mitra Mazumder, Anil Chandewar

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The present study evaluates the possible drug interaction between glimepiride (GLIM) and sildenafil citrate (SIL) in streptozotocin (STZ) induced in diabetic nephropathic (DN) animals and also postulates the possible mechanism of interaction by molecular modeling studies. Diabetic nephropathy was induced by single dose of STZ (60 mg/kg, ip) and confirms it by assessing the blood and urine biochemical parameters on 28th day of its induction. Selected DN animals were used for the drug interaction between GLIM (0.5mg/kg, p.o.) and SIL (2.5 mg/kg, p.o.) after 29th and 70th day of protocol. Drug interaction were assessed by evaluating the plasma drug concentration using HPLC-UV and also determine the change in the biochemical parameter in blood and urine. Mechanism of the interaction was postulated by molecular modeling study using Maestro module of Schrodinger software. DN was confirmed as there was significant alteration in the blood and urine biochemical parameter in STZ treated groups. The concentration of SIL increased significantly (p<0.001) in rat plasma when co administered with GLIM after 70th day of protocol. Molecular modelling study revealed few important interactions with rat serum albumin and CYP2C9.GLIM has strong hydrophobic interaction with binding site residues of rat serum albumin compared to SIL. Whereas, for CYP2C9, GLIM has strong hydrogen bond with polar contacts and hydrophobic interactions than SIL. Present study concludes that bioavailability of SIL increases when co-administered chronically with GLIM in the management of DN animals and mechanism has been supported by molecular modeling studies.

Keywords: diabetic nephropathy, glimepiride, sildenafil citrate, pharmacokinetics, homology modeling, schrodinger

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Authors: Pimporn Thongmuang

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The import value of antimicrobial drugs reached approximately fifteen million Baht in 2010, considered as the highest import value of all modern drugs, and this value is rising every year. Antimicrobials are considered the hazardous drugs by the Ministry of Public Health. This research was conducted in order to investigate the past knowledge of drug use and Antimicrobial drug use behavior. A total of 757 students were selected as the samples out of a population of 1,800 students. This selected students had the experience of Antimicrobial drugs use a year ago. A questionnaire was utilized in this research. The findings put on the view that knowledge gained by the students about proper use of antimicrobial drugs was not brought into practice. This suggests that the education procedure regarding drug use needs adjustment. And therefore the findings of this research are expected to be utilized as guidelines for educating people about the proper use of antimicrobial drugs. At a broader perspective, correct drug use behavior of the public may potentially reduce drug cost of the Ministry of Public Health of Thailand.

Keywords: drug use knowledge, antimicrobial drugs, drug use behavior, drug

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Authors: Mhaned Oubounyt, Jan Baumbach

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Differences in co-expression networks between two or multiple cells (sub)types across conditions is a pressing problem in single-cell RNA sequencing (scRNA-seq). A key challenge is to define those co-variations that differ between or among cell types and/or conditions and phenotypes to examine small regulatory networks that can explain mechanistic differences. To this end, we developed SCANet, an all-in-one Python package that uses state-of-the-art algorithms to facilitate the workflow of a combined single-cell GCN (Gene Correlation Network) and GRN (Gene Regulatory Networks) pipeline, including inference of gene co-expression modules from scRNA-seq, followed by trait and cell type associations, hub gene detection, co-regulatory networks, and drug-gene interactions. In an example case, we illustrate how SCANet can be applied to identify regulatory drivers behind a cytokine storm associated with mortality in patients with acute respiratory illness. SCANet is available as a free, open-source, and user-friendly Python package that can be easily integrated into systems biology pipelines.

Keywords: single-cell, co-expression networks, drug-gene interactions, co-regulatory networks

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Authors: Owonikoko Abayomi Dele

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Epilepsy is an unresolved disease that needs urgent attention. It is a brain disorder that affects over sixty-five (65) million people around the globe. Despite the availability of commercial anti-epileptic drugs, the war against this unmet condition is yet to be resolved. Most epilepsy patients are resistant to available anti-epileptic medications thus the need for affordable novel therapy against epilepsy is a necessity. Numerous phytochemicals have been reported for their potency, efficacy and safety as therapeutic agents against many diseases. This study investigated 99 isolated phytochemicals from Chasmanthera dependens and Carissa edulis against carbonic anhydrase (ii) drug target. The absorption, distribution, metabolism, excretion and toxicity (ADMET) of the isolated compounds were examined using admet SAR-2 web server while Swiss ADME was used to analyze the oral bioavailability, drug-likeness and lead-likeness properties of the selected leads. PASS web server was used to predict the biological activities of selected leads while other important physicochemical properties and interactions of the selected leads with the active site of the target after successful molecular docking simulation with the pyrx virtual screening tool were also examined. The results of these study identified seven lead compounds; C49- alpha-carissanol (-7.6 kcal/mol), C13- Catechin (-7.4 kcal/mol), C45- Salicin (-7.4 kcal/mol), C6- Bisnorargemonine (-7.3 kcal/mol), C36- Pallidine (-7.1 kcal/mol), S4- Lacosamide (-7.1 kcal/mol), and S7- Acetazolamide (-6.4 kcal/mol) for CA II (3HS4 receptor). These leads compounds are probable inhibitors of this drug target due to the observed good binding affinities and favourable interactions with the active site of the drug target, excellent ADMET profiles, PASS Properties, drug-likeness, lead-likeness and oral bioavailability properties. The identified leads have better binding energies as compared to the binding energies of the two standards. Thus, seven identified lead compounds can be developed further towards the development of new anti-epileptic medications.

Keywords: drug-likeness, phytochemicals, carbonic anhydrases, metalloeazymes, active site, ADMET

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Authors: Venkatalakshmi Ranganathan, Ong Hsin Ju, Tan Yinn Ming, Lim Kien Sin, Wong Man Ting, Venkata Srikanth Meka

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The mucoadhesive buccal tablet is an oral drug delivery system which attached to the buccal surface for direct drug absorption into the systemic circulation and the unidirectional drug release is ensured by formulating a hydrophobic backing layer. The objective of present study was to formulate mucoadhesive atenolol bilayer buccal tablets by using sodium alginate, hydroxyethyl cellulose, and xanthan gum as mucoadhesive polymer and the technique applied was direct compression method. Ethyl cellulose was used as backing layer of the tablet. FTIR and DSC analysis were carried out to identify the drug polymer interactions. The prepared tablets were evaluated for physicochemical parameters, ex vivo mucoadhesion time and in-vitro drug release. The formulated tablets showed the average surface pH 6-7 which is favourable for oral mucosa. The formulation containing sodium alginate showed more than 90 % of drug release at the end of the 7 hours in vitro dissolution studies. The formulation containing xanthan gum showed more than 8 hours of mucoadhesion time and all formulation exhibited non fickian release kinetics. The present study indicates enormous potential of erodible mucoadhesive buccal tablet containing atenolol for systemic delivery with an added advantage of circumventing the hepatic first pass metabolism.

Keywords: atenolol, mucoadhesion, in vitro drug release, direct compression, ethyl cellulose

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Authors: Mazen AlGharsan

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Objective: The Carbopol Microsphere of Linezolid, a drug used to treat lung disease (pulmonary disease), was prepared using Buchi B-90 nano spray-drier. Methods: Production yield, drug content, external morphology, particle size, and in vitro release pattern were performed. Results: The work was 79.35%, and the drug content was 66.84%. The surface of the particles was shriveled in shape, with particle size distribution with a mean diameter of 9.6 µm; the drug was released in a biphasic manner with an initial release of 25.2 ± 5.7% at 60 minutes. It later prolonged the release by 95.5 ± 2.5% up to 12 hours. Differential scanning calorimetry (DSC) revealed no change in the melting point of the formulation. Fourier-transform infrared (FT-IR) studies showed no polymer-drug interaction in the prepared nanoparticles.

Keywords: nanotechnology, drug delivery, Linezolid, lung disease

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Authors: Raquel Galante, Marina Braga, Daniela Ghisleni, Terezinha J. A. Pinto, Rogério Colaço, Ana Paula Serro

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The sensitive nature of soft biomaterials, such as hydrogels, renders their sterilization a particularly challenging task for the biomedical industry. Widely used contact lenses are now studied as promising platforms for topical corneal drug delivery. However, to the best of the authors knowledge, the influence of sterilization methods on these systems has yet to be evaluated. The main goal of this study was to understand how different pairs drug-hydrogel would interact under an ozone-based sterilization method in comparison with two conventional processes (steam heat and gamma irradiation). For that, Si-Hy containing hydroxylethyl methacrylate (HEMA) and [tris(trimethylsiloxy)silyl]propyl methacrylate (TRIS) was produced and soaked in different drug solutions, commonly used for the treatment of ocular diseases (levofloxacin, chlorhexidine, diclofenac and timolol maleate). The drug release profiles and main material properties were evaluated before and after the sterilization. Namely, swelling capacity was determined by water uptake studies, transparency was accessed by UV-Vis spectroscopy, surface topography/morphology by scanning electron microscopy (SEM) and mechanical properties by performing tensile tests. The drug released was quantified by high performance liquid chromatography (HPLC). The effectiveness of the sterilization procedures was assured by performing sterility tests. Ozone gas method led to a significant reduction of drug released and to the formation of degradation products specially for diclofenac and levofloxacin. Gamma irradiation led to darkening of the loaded Si-Hys and to the complete degradation of levofloxacin. Steam heat led to smoother surfaces and to a decrease of the amount of drug released, however, with no formation of degradation products. This difference in the total drug released could be the related to drug/polymer interactions promoted by the sterilization conditions in presence of the drug. Our findings offer important insights that, in turn, could be a useful contribution to the safe development of actual products.

Keywords: drug delivery, silicone hydrogels, sterilization, gamma irradiation, steam heat, ozone gas

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Authors: Maryam Parsian, Pelin Mutlu, Ufuk Gunduz

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Two-dimensional cell culture affords simplicity and low cost, but it has serious limitations; lacking cell-cell and cell-matrix interactions that are present in tissues. Cancer cells grown in 3D culture systems have distinct phenotypes of adhesion, growth, migration, invasion as well as profiles of gene and protein expression. These interactions cause the 3D-cultured cells to acquire morphological and cellular characteristics relevant to in vivo tumors. Palbociclib is a chemotherapeutic agent for the treatment of ER-positive and HER-negative metastatic breast cancer. Poly-amidoamine (PAMAM) dendrimer is a well-defined, special three-dimensional structure and has a multivalent surface and internal cavities that can play an essential role in drug delivery systems. In this study, palbociclib is loaded onto the magnetic PAMAM dendrimer. Hanging droplet method was used in order to form 3D spheroids. The possible toxic effects of both free drug and drug loaded nanoparticles were evaluated in 2D and 3D MCF-7, MD-MB-231 and SKBR-3 breast cancer cell culture models by performing MTT cell viability and Alamar Blue assays. MTT analysis was performed with six different doses from 1000 µg/ml to 25 µg/ml. Drug unloaded PAMAM dendrimer did not demonstrate significant toxicity on all breast cancer cell lines. The results showed that 3D spheroids are clearly less sensitive than 2D cell cultures to free palbociclib. Also, palbociclib loaded PAMAM dendrimers showed more toxic effect than free palbociclib in all cell lines at 2D and 3D cultures. The results suggest that the traditional cell culture method (2D) is insufficient for mimicking the actual tumor tissue. The response of the cancer cells to anticancer drugs is different in the 2D and 3D culture conditions. This study showed that breast cancer cells are more resistant to free palbociclib in 3D cultures than in 2D cultures. However, nanoparticle loaded drugs can be more cytotoxic when compared to free drug.

Keywords: 2D and 3D cell culture, breast cancer, palbociclibe, PAMAM magnetic nanoparticles

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Authors: Farzad Jalilian, Mehdi Mirzaei Alavijeh, Fazel Zinat Motlagh

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Social support is an important benchmark of health for people in avoidance conditions. The main goal of this study was to determine the three kinds of social support (family, friend and other significant) to drug cessation among male addicts, in Kermanshah, the west of Iran. This cross-sectional study was conducted among 132 addicts, randomly selected to participate voluntarily in the study. Data were collected from conduct interviews based on standard questionnaire and analyzed by using SPSS-18 at 95% significance level. The majority of addicts were young (Mean: 30.4 years), and with little education. Opium (36.4%), Crack (21.2%), and Methamphetamine (12.9%) were the predominant drugs. Inabilities to reject the offer and having addict friends are the most often reasons for drug usage. Almost, 18.9% reported history of drug injection. 43.2% of the participants already did drug cessation at least once. Logistic regression showed the family support (OR = 1.110), age (OR = 1.106) and drug use initiation age (OR = 0.918) was predicting drug cessation. Our result showed; family support is a more important effect among types of social support in drug cessation. It seems that providing educational program to addict’s families for more support of patients at drug cessation can be beneficial.

Keywords: drug cessation, family support, drug use, initiation age

Procedia PDF Downloads 551

Authors: Temesgen Geremew

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Functionalized nanoparticles have emerged as a revolutionary platform for drug delivery, offering significant advantages over traditional methods. By strategically modifying their surface properties, these nanoparticles can be designed to target specific tissues and cells, significantly reducing off-target effects and enhancing therapeutic efficacy. This targeted approach allows for lower drug doses, minimizing systemic exposure and potential side effects. Additionally, functionalization enables controlled release of the encapsulated drug, improving drug stability and reducing the frequency of administration, leading to improved patient compliance. This work explores the immense potential of functionalized nanoparticles in revolutionizing drug delivery, addressing limitations associated with conventional therapies and paving the way for personalized medicine with precise and targeted treatment strategies.

Keywords: nanoparticles, drug, nanomaterials, applications

Procedia PDF Downloads 69