Correlation Between In Silico Physicochemical Characteristics of Drugs and Their Mean Residence Time in Human and Dog

2016

In Silico Pharmacol.

Self Cite

BackgroundTotal body clearance of biological drugs is for the most part dependent on the receptor mechanisms (receptor mediated clearance) and the concentration of antibodies aimed at administered drug – anti-drug-antibodies (ADA). One of the significant factors that induces the increase of ADA level after drug administration could be the aggregates present in the finished product or formed in the organism. Numerous attempts have been made to identify the sequence fragments that could be responsible for forming the aggregates – aggregate prone regions (APR).PurposeThe aim of this study was to find physiochemical parameters specific to APR that would differentiate APR from other sequences present in therapeutic proteins.MethodsTwo groups of amino acid sequences were used in the study. The first one was represented by the sequences separated from the therapeutic proteins (n = 84) able to form APR. A control set (CS) consisted of peptides that were chosen based on 22 tregitope sequences.ResultsClassification model and four classes (A, B, C, D) of sequences were finally presented. For model validation Cooper statistics was presented.ConclusionsThe study proposes a classification model of APR. This consists in a distinction of APR from sequences that do not form aggregates based on the differences in the value of physicochemical parameters. Significant share of electrostatic parameters in relation to classification model was indicated.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Classification model of amino acid sequences prone to aggregation of therapeutic proteins

Marczak¹,

Okoniewska²,

2016

In Silico Pharmacol.

Self Cite

“…This part of the analysis did not give the expected results. Therefore in the second phase of the analysis, the arithmetic expressions based on the physicochemical properties of our sequences were formulated according to the earlier published method [35]. The arithmetic statements consisting of several related physicochemical properties allowed this study to find a statistically significant correlation with LogIC 50 .…”

Section: Statistical Analysis and Model Validationmentioning

confidence: 99%

Relationship between Tregitopes Structure and Binding with Major Histocompatibility Complex Class I

Okoniewska¹,

Okoniewski²,

2015

Drug Res (Stuttg)

Epitopes of T-cells (tregitopes) are linear sequences of amino acids present in many animal and human proteins. Tregitopes suppress the immunological response and could play a significant regulatory role in the pathogenesis of autoimmune diseases. They modulate T-cell response activated by the antigens of the major histocompatibility complex class I (MHC-I).The aim of this study was an attempt to determine the correlation between physicochemical properties and structures of tregitopes and their binding strength with MHC-I. 21 amino acid sequences of immunoglobulin G with verified or similar to tregitopes function were selected. The analysis of the binding strength with MHC-I was carried out on 41 various alleles since MHC-I can be coded by numerous alleles. The first phase of study attempted to find a correlation between the half minimal inhibitory concentration (LogIC50) calculated for MHC-I and physicochemical properties. From formulated arithmetic statements, only one allowed to determine significant correlation with LogIC50 with reference to alleles A*02:01 and A*02:06. The correlations for the alleles were linear and sigmoid, respectively (p<0.001). The presence of the repeated amino acids was confirmed in the sequences of the studied compounds. These amino acids are connected with stronger binding or lack of the binding with MHC-I expressed by LogIC50. The study shows the translation from the classification (cloud) model to the linear one. The significant linear dependence between chemical structure of tregitopes and their LogIC50 calculated for MHC-I was displayed. The presented method can be used in screening of new sequences that have regulatory properties for regulatory T-cells.

“…In case of the compounds that are chiral or undergo tautomerization, up to 32 stereoisomer calculations were generated for each compound or tautomer. The used software allowed for the calculations of 51 parameters [19].…”

Section: In Silico Calculationsmentioning

confidence: 99%

“…The values of these expressions were correlated with the value of MIC 50 , and MIC 90 of Streptococcus spp. using a previously described method [19]. For arithmetic expressions with significant correlations (coefficient determination > 0.85), internal and external validation was performed [20,21].…”

Section: Model Developmentmentioning

confidence: 99%

Relationship between Tetracyclines’ Structure and Minimal Inhibitory Concentration of Streptococcus spp.

Marczak¹,

Feder

2014

Drug Res (Stuttg)

During the past years, a growing number of bacterial strains have become resistant to tetracyclines. The problem of increasing resistance and lack of susceptibility to tetracyclines applies to strains isolated from both: animals and humans. Basic tools to design new drugs and determining the direction of the search for new molecules is the analysis of the relationship between the chemical structure and the pharmacokinetic and pharmacodynamic parameters. Purpose of this study is to determine the relationship between physicochemical parameters of tetracyclines and MIC50 and MIC90 values determined for Streptococcus spp. Analysis of physicochemical parameters of selected drugs was made using MarvinSketch 5.11.5 (ChemAxon Ltd.) and QuickProp 3.1 software from Schrödinger package v 31207. MIC50 and MIC90 values were correlated with 51 calculated physicochemical parameters and arithmetic expressions. Internal and external model validation was performed using leave-one out method. 4 arithmetic expressions fulfilled all validation criteria, but only in relation to MIC50. A new method to optimize the tetracyclines' structure in relation to Streptococcus spp. was presented. It was also shown that the relations of structure: antimicrobial activity type can have different nature depending on MIC50 or MIC90 of specific bacterial strain.