scholarly journals QPhAR – Quantitative Pharmacophore Activity Relationship: Method and Validation

2021 ◽  
Author(s):  
Stefan M. Kohlbacher ◽  
Thierry Langer ◽  
Thomas Seidel
Keyword(s):  
Drug Discovery ◽  
Cross Validation ◽  
Pharmacophore Model ◽  
Drug Approval ◽  
Interaction Patterns ◽  
Molecular Features ◽  
Lead Optimisation ◽  
Drug Approval Process ◽  
High Level ◽  
Pharmacophore Models

Abstract QSAR methods are widely applied in the drug discovery process, both in the hit‑to‑lead and lead optimization phase, as well as in the drug-approval process. Most QSAR algorithms are limited to using molecules as input and disregard pharmacophores or pharmacophoric features entirely. However, due to the high level of abstraction, pharmacophore representations provide some advantageous properties for building quantitative SAR models. The abstract depiction of molecular interactions avoids a bias towards overrepresented functional groups in small datasets. Furthermore, a well‑crafted quantitative pharmacophore model can generalise to underrepresented or even missing molecular features in the training set by using pharmacophoric interaction patterns only. This paper presents a novel method to construct quantitative pharmacophore models and demonstrates its applicability and robustness on more than 250 diverse datasets. 5‑fold cross-validation on these datasets with default settings yielded an average RMSE of 0.62, with an average standard deviation of 0.18. Additional cross-validation studies on datasets with 15-20 training samples showed that robust quantitative pharmacophore models could be obtained. These low requirements for dataset sizes renders quantitative pharmacophores a viable go-to method for medicinal chemists, especially in the lead-optimisation stage of drug discovery projects.

scholarly journals QPHAR: quantitative pharmacophore activity relationship: method and validation

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Stefan M. Kohlbacher ◽  
Thierry Langer ◽  
Thomas Seidel
Keyword(s):  
Drug Discovery ◽  
Cross Validation ◽  
Pharmacophore Model ◽  
Drug Approval ◽  
Interaction Patterns ◽  
Molecular Features ◽  
Lead Optimisation ◽  
Drug Approval Process ◽  
High Level ◽  
Pharmacophore Models

AbstractQSAR methods are widely applied in the drug discovery process, both in the hit‐to‐lead and lead optimization phase, as well as in the drug-approval process. Most QSAR algorithms are limited to using molecules as input and disregard pharmacophores or pharmacophoric features entirely. However, due to the high level of abstraction, pharmacophore representations provide some advantageous properties for building quantitative SAR models. The abstract depiction of molecular interactions avoids a bias towards overrepresented functional groups in small datasets. Furthermore, a well‐crafted quantitative pharmacophore model can generalise to underrepresented or even missing molecular features in the training set by using pharmacophoric interaction patterns only. This paper presents a novel method to construct quantitative pharmacophore models and demonstrates its applicability and robustness on more than 250 diverse datasets. fivefold cross-validation on these datasets with default settings yielded an average RMSE of 0.62, with an average standard deviation of 0.18. Additional cross-validation studies on datasets with 15–20 training samples showed that robust quantitative pharmacophore models could be obtained. These low requirements for dataset sizes render quantitative pharmacophores a viable go-tomethod for medicinal chemists, especially in the lead-optimisation stage of drug discovery projects.

MARKETING AUTHORIZATION OF GENERIC DRUG: GLOBAL ISSUE AND CHALLENGES

2013 ◽  
Vol 1 (3) ◽  
pp. 1-6
Author(s):  
Pankaj Kashyap ◽  
Eshant Duggal ◽  
Parveen Budhwar ◽  
Jitendra Kumar Badjatya
Keyword(s):  
Generic Drug ◽  
Patent Protection ◽  
Drug Approval ◽  
Approval Process ◽  
Generic Medicines ◽  
Healthcare Provision ◽  
Pharmaceutical Markets ◽  
Global Issue ◽  
Drug Approval Process ◽  
High Level

Generic medicines are those whose patent protection has expired, and which may be produced by manufacturers otherthan the innovator company. Use of generic medicines has been increasing in recent years, primarily as a cost savingmeasure in healthcare provision. Generic medicines are typically 20 to 90% cheaper than originator equivalents. Theobjective is to provide a high-level description of what generic medicines are and how they differ, at a regulatory andlegislative level, from originator medicines. It describes the current and historical regulation of medicines in theworld’s two main pharmaceutical markets, in addition to the similarities, as well as the differences, between genericsand their originator equivalents including the reasons for the cost differences seen between originator and genericmedicines. This article refers to the general generic drug approval process in India, USA, and Japan. They havedifferent regulation and approval process. 

scholarly journals Computational and micro-analytical techniques to study the in vitro and in silico models of novel therapeutic drugs

2016 ◽  
Author(s):  
◽  
Njabulo Joyfull Gumede
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Pharmacophore Model ◽  
Binding Affinities ◽  
Ic50 Values ◽  
Database Screening ◽  
Pharmacophore Models ◽  
Novel Therapeutic

In drug discovery and development projects, metabolism of new chemical entities (NCEs) is a major contributing factor for the withdrawal of drug candidates, a major concern for other chemical industries where chemical-biological interactions are involved. NCEs interact with a target macro-molecule to stimulate a pharmacological or toxic response, known as pharmacodynamics (PD) effect or through the Adsorption, Distribution, Metabolism, and Excretion (ADME) process, triggered when a bio-macromolecule interacts with a therapeutic drug. Therefore, the drug discovery process is important because 75% of diseases known to human kind are not all cured by therapeutics currently available in the market. This is attributed to the lack of knowledge of the function of targets and their therapeutic use in order to design therapeutics that would trigger their pharmacological responses. Accordingly, the focus of this work is to develop cost saving strategies for medicinal chemists involved with drug discovery projects. Therefore, studying the synergy between in silico and in vitro approaches maybe useful in the discovery of novel therapeutic compounds and their biological activities. In this work, in silico methods such as structure-based and ligand-based approaches were used in the design of the pharmacophore model, database screening and flexible docking methods. Specifically, this work is presented by the following case studies: The first involved molecular docking studies to predict the binding modes of catechin enantiomer to human serum albumin (HSA) interaction; the second involved the use of docking methods to predict the binding affinities and enantioselectivity of the interaction of warfarin enantiomers to HSA. the third case study involved a combined computational strategy in order to generate information on a diverse set of steroidal and non-steroidal CYP17A1 inhibitors obtained from literature with known experimental IC50 values. Finally, the fourth case study involved the prediction of the site of metabolisms (SOMs) of probe substrates to Cytochrome P450 metabolic enzymes CYP 3A4, 2D6, and 2C9 making use of P450 module from Schrödinger suite for ADME/Tox prediction. The results of case study I were promising as they were able to provide clues to the factors that drive the synergy between experimental kinetic parameters and computational thermodynamics parameters to explain the interaction between drug enantiomers and thetarget protein. These parameters were correlated/converted and used to estimate the pseudo enantioselectivity of catechin enantiomer to HSA. This approach of combining docking methodology with docking post-processing methods such as MM-GBSA proved to be vital in estimating the correct pseudo binding affinities of a protein-ligand complexes. The enantioselectivity for enantiomers of catechin to HSA were 1,60 and 1,25 for site I and site II respectively. The results of case study II validates and verifies the preparation of ligands and accounting for tautomers at physiological pH, as well as conformational changes prior to and during docking with a flexible protein. The log KS = 5.43 and log KR = 5.34 for warfarin enantiomer-HSA interaction and the enantioselectivity (ES = KS/KR) of 1.23 were close to the experimental results and hence referred to as experimental-like affinity constants which validated and verified their applicability to predict protein-ligand binding affinities. In case study III, a 3D-QSAR pharmacophore model was developed by using 98 known CYP17A1 inhibitors from the literature with known experimental IC50 values. The starting compounds were diverse which included steroidal and non-steroidal inhibitors. The resulting pharmacophore models were trained with 69 molecules and 19 test set ligands. The best pharmacophore models were selected based on the regression coefficient for a best fit model with R2 (ranging from 0.85-0.99) & Q2 (ranging from 0.80-0.99) for both the training and test sets respectively, using Partial Least Squares (PLS) regression. On the other hand, the best pharmacophore model selected was further used for a database screening of novel inhibitors and the prediction of their CYP17A1 inhibition. The hits obtained from the database searches were further subjected to a virtual screening workflow docked to CYP17A1 enzyme in order to predict the binding mode and their binding affinities. The resulting poses from the virtual screening workflow were subjected to Induced Fit Docking workflow to account for protein flexibility during docking. The resulting docking poses were examined and ranked ordered according to the docking scores (a measure of affinity). Finally, the resulting hits designed from an updated model from case study III were further synthesized in an external organic chemistry laboratory and the synthetic protocols as well as spectroscopic data for structure elucidation forms part of the provisional patent specification. A provisional patent specification has been filed (RSA Pat. Appln. 2015/ 07849). The case studies performed in this thesis have enabled the discovery of non-steroidal CYP17A1 inhibitors.

COMPARATIVE STUDY FOR GENERIC DRUG APPROVAL PROCESS AND THEIR REGISTRATION AS PER CTD IN EUROPE, USA AND BRAZIL

2016 ◽  
Vol 4 (2) ◽  
pp. 1-9
Author(s):  
Lincy Joseph ◽  
Mathew George ◽  
Kalpesh K Malaviya ◽  
Kalpesh K Malaviya ◽  
Bincy K Chacko ◽  
...  
Keyword(s):  
Generic Drug ◽  
Drug Approval ◽  
Evaluation Agency ◽  
Approval Process ◽  
Registration Process ◽  
Regulatory Guidelines ◽  
Food Drug Administration ◽  
Government Websites ◽  
Drug Approval Process ◽  
Regulatory Sites

This aims to compare the generic drug approval and registration process in the regulatory market of Europe, USA andBrazil. Based on the information collected from various sources such as regulatory sites, Government websites,discussion with regulatory agent, interviewing pharma professionals and literature survey from various journals, aclear picture on the generic drug approval and registration process of each country was drawn. The differentauthorities’ viz. European Medicines Evaluation Agency (EMEA) of Europe, Food Drug Administration (FDA) ofUSA and National Health Surveillance Agency (ANVISA) of Brazil carried out the generic drug approval andregistration process in the respective countries. After analysing the various requirements for the generic drug approvalin the above stated countries, it was concluded that the regulatory guidelines of Europe and Brazil was not welldefined. But FDA gives very much well defined requirements. 

Role of Topical Oxymetazoline for Management of Erythematotelangiectatic Rosacea

2017 ◽  
Vol 52 (3) ◽  
pp. 263-267 ◽  
Author(s):  
Rebecca M. Hoover ◽  
John Erramouspe
Keyword(s):  
English Language ◽  
Human Subjects ◽  
Data Extraction ◽  
Drug Approval ◽  
Current Data ◽  
Patient Response ◽  
Study Selection ◽  
Cochrane Database ◽  
Drug Approval Process ◽  
Individual Patient Response

Objective: To review and summarize topical oxymetazoline’s pharmacology, pharmacokinetics, efficacy, safety, cost, and place in therapy for persistent redness associated with erythematotelangiectatic rosacea. Data Sources: Literature searches of MEDLINE (1975 to September 2017), International Pharmaceutical Abstracts (1975 to September 2017), and Cochrane Database (publications through September 2017) using the terms rosacea, persistent redness, α -agonist, and oxymetazoline. Study Selection and Data Extraction: Results were limited to studies of human subjects, English-language publications, and topical use of oxymetazoline. Relevant materials from government sources, industry, and reviews were also included. Data Synthesis: Data support the efficacy of oxymetazoline for persistent facial redness. Little study beyond clinical trials cited in the drug approval process has been conducted. Current data suggest that oxymetazoline is similar in safety and efficacy to brimonidine. Head-to-head comparisons of topical α-agonists for erythema caused by rosacea are needed. Conclusion: The topical α-agonist, oxymetazoline, is safe and effective for reducing persistent facial redness associated with erythematotelangiectatic subtype of rosacea. Health care practitioners selecting among treatments should consider not only the subtype of rosacea but also individual patient response, preference, and cost.

Association of genes copy number in extracellular DNA in patients with prostate cancer and sensitivity to radiotherapy.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e15014-e15014
Author(s):  
Denis S. Kutilin ◽  
Mikhail S. Zinkovich ◽  
Marina A. Gusareva ◽  
Aleksandr V. Faenson ◽  
Elena A. Karnauhova ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Blood Plasma ◽  
Copy Number ◽  
Extracellular Dna ◽  
Biochemical Relapse ◽  
Prostate Tumors ◽  
Molecular Features ◽  
Number Variation ◽  
Invasive Method ◽  
High Level

e15014 Background: Radiotherapy (RT) is one of the main treatments for prostate cancer (PC). The effectiveness of such therapy depends on the initial radioresistance of tumor cells, which is ensured by their certain molecular features, which include the genes copy number variation (CNV). Model experiments on cell cultures (obtained from surgical material) have shown that CNVs have high potential as predictors of RT sensitivity. However, this potential is limited by the high level of invasiveness in obtaining biomaterials. A possible solution to this problem lies in the transition to CNV study in the extracellular DNA (cfDNA) of blood plasma. The aim of the study was to screen predictors of radioresistant PC based on the genes CNV in cfDNA. Methods: The study included 400 patients with diagnosed PC (T2a-3bN0M0, st. II-III), 40 of them after RT had a state of biochemical relapse (RT was performed on a Novalis TX linear accelerator (Varian, USA) (TFDisoeff = 75 Gr), mean time to biochemical relapse 7.5 months). Blood samples were separated into plasma and cell fraction by centrifugation. Isolation of cfDNA from blood plasma was performed using a set of reagents “DNA-Plasma-M” (Russia). Determination of the relative CNV of 13 genes (CDK1, CCND3, CDKN1B, TP53, PTEN, BCL2, XRCC4, BAX, RBBP8, H2AX, BRCA2, RAD50, EP300) was performed using the Real-Time qPCR method. Differences were assessed using the Mann-Whitney test; the Benjamin-Hochberg correction was used to correct multiple comparisons. Results: In the group with biochemical relapse (n = 40), the CNV of genes CDK1, CDKN1B, RBBP8, XRCC4, BRCA2 and RAD50 was statistically significantly (p < 0.05) higher by 2.0 times, 2.3 times, 2.1 times, 1.4 times, 2.4 times and 2.8 times, respectively, relative to the CNV of these genes in the cfDNA of the group without relapse (n = 360). Conclusions: Thus, it was found that the CNV of 6 genes (CDK1, CDKN1B, RBBP8, XRCC4, BRCA2 and RAD50) may be a potential molecular marker of radiosensitivity of prostate tumors. Based on the obtained data, a low invasive method for determining the prostate tumors sensitivity to RT has been developed.

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