scholarly journals In silico assessment of chronic toxicity of a combination drug namely ‘Olmesartan medoxomil and Hydrochlorothiazide’, marketed in Bangladesh

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Matrika Saha Roy ◽  
Rehnuma Tanjin ◽  
Tanmoy Debnath ◽  
Bidduth Kumar Sarkar ◽  
Prema Modak ◽  
...  
Keyword(s):  
Combination Therapy ◽  
Binding Affinity ◽  
Energy Minimization ◽  
In Silico ◽  
Protein Structures ◽  
Olmesartan Medoxomil ◽  
Drug Combinations ◽  
The Body ◽  
Fixed Dose ◽  
Combination Drug

Abstract Background Nowadays combination therapy has become more popular due to their additional effect, synergistic effect and antagonistic effect. Any of these can influence the treatment profile. Combination therapy is used to treat some chronic diseases like diabetes, hypertension, cancer etc. But recently India has banned some fixed dose drug combinations due to their increased chances of adverse drug effects and drug interactions. So it is the time to take a look on the present drug combinations available in Bangladesh. An in silico study may provide important information about their probable toxicities. Drugs available in the combination may deposit slowly in the body and may lead to toxicities. Here an antihypertensive drug combination ‘Olmesartan medoxomil and Hydrochlorothiazide’ had been studied. Results Olmesartan medoxomil and Hydrochlorothiazide have not been found to comply any similar protein to interact with each other, thus no possible chance of additional toxicity of the combination in case of long term use. Conclusions At first, using PubChem the ligand was searched for a canonical SMILE. By inputting the canonical SMILE in Protox, a basic information about toxicities was predicted. From Swiss Target Prediction, target proteins responsible for both efficacy and toxicity were identified. These protein structures were downloaded from Protein Data Bank and edited with Flare. Undesired amino acid, ligand–ligand complex, fatty acid, and water molecules were removed by PyMOL. Structurally modified proteins and ligands were inputted in Swiss PDB viewer for energy minimization. Energy minimization is a very important step because unfavorable bond length, bond strength and torsion angle between protein and ligand may interfere with docking procedure. Then docking between Olmesartan medoxomil (ligand) and the proteins responsible for efficacy and toxicity was performed by PyRx. Vina binding affinity provided the value of binding strength between the ligand and the proteins, which determines how strong the bond is. The more negative the vina binding affinity, the stronger the bond. Discovery studio software was used to visualize the docking complexes. Same steps were followed for Hydrochlorothiazide to identify proteins responsible for desired and undesired effects, but no toxic effect was found from protox.

scholarly journals Pengaruh Minimisasi Energi MMFF94 dengan MarvinSketch dan Open Babel PyRx pada Penambatan Molekular Turunan Oksindola Tersubstitusi

ALCHEMY ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 33-40
Author(s):  
Atika Umi Hanif ◽  
Prima Agusti Lukis ◽  
Arif Fadlan
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Force Field ◽  
Binding Affinity ◽  
Energy Minimization ◽  
In Silico ◽  
Initial State ◽  
Molecular Force ◽  
Molecular Force Field ◽  
Docking Energy

 In silico technique is widely used for drug discovery because it can predict the conformation of ligands in protein macromolecules and it can calculate the binding affinity. The energy minimization is carried out to make the ligand more stable near the initial state during molecular docking process. The Merck Molecular Force Field (MMFF94) is one type of energy minimization process often used in organic compounds. The molecular docking of substituted oxindole derivatives on indoleamine macromolecules 2,3-dioxygenase (IDO-1, PDB: 2D0T) by MMFF94 minimization operated by MarvinSketch and Open Babel in PyRx showed different results. The binding affinity energy obtained was also quite different, but the ligands have the same conformation and bind the same residue with slightly different bond distances. Keywords: Molecular docking, energy minimization, substituted oxindole, Merck Molecular Force Field 94  Teknik in silico banyak digunakan untuk penemuan senyawa obat karena dapat memprediksi konformasi suatu ligan dalam makromolekul protein dan mampu menghitung nilai afinitas ikatan. Proses minimisasi energi dilakukan untuk menjadikan ligan lebih stabil mendekati keadaan awal selama penambatan molekular berlangsung. Merck Molecular Force Field (MMFF94) adalah salah satu jenis persamaan minimisasi energi yang sering digunakan pada senyawa organik. Hasil pengujian pengaruh minimisasi energi dengan MMFF94 menggunakan program MarvinSketch dan Open Babel dalam PyRx pada turunan oksindola tersubstitusi alkil terhadap makromolekul 2,3-dioxygenase indoleamine (IDO-1, PDB: 2D0T) menunjukkan hasil dengan nilai yang berbeda. Energi afinitas ikatan yang didapatkan juga cukup berbeda, namun ligan memiliki konformasi yang sama dan mengikat residu yang sama dengan jarak ikatan yang sedikit berbeda. Kata kunci: Penambatan molekular, minimisasi energi, oksindola tersubstitusi, Merck Molecular Force Field 94

scholarly journals Triple fixed combination for hypertension treatment — a green light in the new european Guidelines 2018

2019 ◽  
Vol 24 (6) ◽  
pp. 716-722
Author(s):  
A. O. Konradi
Keyword(s):  
Combination Therapy ◽  
Arterial Hypertension ◽  
European Society ◽  
Green Light ◽  
Drug Combinations ◽  
Fixed Dose ◽  
Multiple Drug ◽  
Triple Combination Therapy ◽  
European Guidelines ◽  
European Society Of Cardiology

The majority of patients with stable arterial hypertension require combination therapy which is supported by the clinical evidence. The established target levels of blood pressure below 130/80 mmHg are challenging and demand multiple drug combinations in a single patient. Therefore, the use of dual and triple combination therapy is getting wider, and rational triple fixed combinations are highly relevant. The updated guidelines on the diagnostics, management and treatment of arterial hypertension of the European Society of Hypertension and the European Society of Cardiology confirm and recommend early and wider use of the fixed-dose drug combinations. The paper reviews the main practical issues of the use of combination therapy, including key questions of the change from free dose to fixed dose combinations and their rational choice.

Lipegfilgrastim: A long-acting, once-per-cycle, glycopegylated recombinant human filgrastim.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e13548-e13548 ◽  
Author(s):  
Christian Scheckermann ◽  
Karsten Schmidt ◽  
Afsaneh Abdolzade-Bavil ◽  
Hermann Allgaier ◽  
Udo W. Mueller ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Specific Activity ◽  
The Body ◽  
Biologically Active ◽  
Severe Neutropenia ◽  
Fixed Dose ◽  
Protein Activity ◽  
Reactive Groups ◽  
Long Acting

e13548 Background: Lipegfilgrastim is a once-per-cycle fixed-dose glycoPEGylated granulocyte-colony stimulating factor (G-CSF) under development for the prevention of severe neutropenia in cancer patients receiving chemotherapy (CTx). PEGylation of a molecule extends its half-life in the body, requiring less frequent dosing and allowing for administration of G-CSF once per CTx cycle, making treatment potentially less expensive and enhancing patient compliance and safety. Briefly, traditional PEGylation methods use chemical conjugation through reactive groups on amino acids, which may reduce protein activity and result in non-uniform chemical and pharmaceutical properties. Here, we describe a highly site-specific GlycoPEGylation technology for site-directed PEGylation and summarize preclinical findings compared with pegfilgrastim (Neulasta). Methods: Glycosylation sequon scanning was used to identify the glycoPEGylation site with least impact on protein activity. E. coli-expressed G-CSF was selectively glycosylated at natural O-glycosylation sites and a polyethylene glycol (PEG) sialic acid derivative attached using a sialyltransferase (glycoPEGylation technology). Ligand binding affinity was assessed using the BIACORE 3000 system. Biologic activity of lipegfilgrastim was assessed in an NFS-60 cell line proliferation assay vs filgrastim and pegfilgrastim. PK and PD properties were studied in healthy and neutropenic animal models. Results: GlycoPEGylation produces long-acting G-CSF with improved PK profiles. In vitro, lipegfilgrastim had binding affinity and specific activity comparable to pegfilgrastim, and both were lower than non-PEGylated filgrastim. Comparable increases in leukocytes, neutrophilic granulocytes, and monocytes were seen with lipegfilgrastim and pegfilgrastim in rats and monkeys and were consistent with the effects expected for a long-lasting G-CSF. Conclusions: GlycoPEGylation technology platform is used to produce lipegfilgrastim – a novel, biologically active G-CSF with greater structural homogeneity and comparable pharmacologic properties to conventionally PEGylated G-CSFs.

Artificial intelligence in drug combination therapy

2018 ◽  
Vol 20 (4) ◽  
pp. 1434-1448 ◽  
Author(s):  
Igor F Tsigelny
Keyword(s):  
Artificial Intelligence ◽  
Combination Therapy ◽  
Drug Combination ◽  
Drug Combinations ◽  
Machine Learning Algorithms ◽  
Genetic Profile ◽  
Combination Drug ◽  
Area Of Interest ◽  
Drug Combination Therapy ◽  
Optimal Drug

AbstractCurrently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a physician often meets a patient having a genomic profile including more than five molecular aberrations. Drug combination therapy has been an area of interest for a while, for example the classical work of Loewe devoted to the synergism of drugs was published in 1928—and it is still used in calculations for optimal drug combinations. More recently, over the past several years, there has been an explosion in the available information related to the properties of drugs and the biomedical parameters of patients. For the drugs, hundreds of 2D and 3D molecular descriptors for medicines are now available, while for patients, large data sets related to genetic/proteomic and metabolomics profiles of the patients are now available, as well as the more traditional data relating to the histology, history of treatments, pretreatment state of the organism, etc. Moreover, during disease progression, the genetic profile can change. Thus, the ability to optimize drug combinations for each patient is rapidly moving beyond the comprehension and capabilities of an individual physician. This is the reason, that biomedical informatics methods have been developed and one of the more promising directions in this field is the application of artificial intelligence (AI). In this review, we discuss several AI methods that have been successfully implemented in several instances of combination drug therapy from HIV, hypertension, infectious diseases to cancer. The data clearly show that the combination of rule-based expert systems with machine learning algorithms may be promising direction in this field.

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