Dataset Information

Comparison of nine programs predicting pK(a) values of pharmaceutical substances.

ABSTRACT: Knowledge of the possible ionization states of a pharmaceutical substance, embodied in the pK(a) values (logarithm of the acid dissociation constant), is vital for understanding many properties essential to drug development. We compare nine commercially available or free programs for predicting ionization constants. Eight of these programs are based on empirical methods: ACD/pK(a) DB 12.0, ADME Boxes 4.9, ADMET Predictor 3.0, Epik 1.6, Marvin 5.1.4, Pallas pKalc Net 2.0, Pipeline Pilot 5.0, and SPARC 4.2; one program is based on a quantum chemical method: Jaguar 7.5. We compared their performances by applying them to 197 pharmaceutical substances with 261 carefully determined and highly reliable experimental pK(a) values from a literature source. The programs ADME Boxes 4.9, ACD/pK(a) DB 12.0, and SPARC 4.2 ranked as the top three with mean absolute deviations of 0.389, 0.478, and 0.651 and r(2) values of 0.944, 0.908, and 0.894, respectively. ACD/pK(a) DB 12.0 predicted all sites, whereas ADME Boxes 4.9 and SPARC 4.2 failed to predict 5 and 18 sites, respectively. The performance of the quantum chemical-based program Jaguar 7.5 was not as expected, with a mean absolute deviation of 1.283 and an r(2) value of 0.579, indicating the potential for further development of this type of approach to pK(a) prediction.

SUBMITTER: Liao C

PROVIDER: S-EPMC7289148 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comparison of nine programs predicting pK(a) values of pharmaceutical substances.

Liao Chenzhong C Nicklaus Marc C MC

Journal of chemical information and modeling 20091201 12

Knowledge of the possible ionization states of a pharmaceutical substance, embodied in the pK(a) values (logarithm of the acid dissociation constant), is vital for understanding many properties essential to drug development. We compare nine commercially available or free programs for predicting ionization constants. Eight of these programs are based on empirical methods: ACD/pK(a) DB 12.0, ADME Boxes 4.9, ADMET Predictor 3.0, Epik 1.6, Marvin 5.1.4, Pallas pKalc Net 2.0, Pipeline Pilot 5.0, and ...[more]

PMID: 19961204

Similar Datasets

Project description:Danofloxacin is a synthetic fluoroquinolone with broad-spectrum activity developed for use in veterinary medicine. The aim of this study was to evaluate the pharmacokinetic/pharmacodynamic (PK/PD) targets, PK/PD cutoff values and the optimum doses of danofloxacin against P. multocida and H. parasuis in piglets. Single dose serum pharmacokinetics was determined in piglets after intravenous and intramuscular administration of 2.5 mg/kg. Danofloxacin was well absorbed and fully bioavailable (95.2%) after intramuscular administration of 2.5 mg/kg. The epidemiological cutoff (ECOFF) values of danofloxacin from 931 P. multocida isolates and 263 H. parasuis isolates were 0.03 and 4 mg/L, respectively. Danofloxacin MICs determined in porcine serum were markedly lower than those measured in artificial broth, with a broth/serum ratio of 4.33 for H. parasuis. Compared to P. multocida, danofloxacin exhibited significantly longer post-antibiotic effects (3.18-6.60 h) and post-antibiotic sub-MIC effects (7.02-9.94 h) against H. parasuis. The mean area under the concentration-time curve/MIC (AUC24h/MIC) targets of danofloxacin in serum associated with the static and bactericidal effects were 32 and 49.8, respectively, for P. multocida, whereas they were 14.6 and 37.8, respectively, for H. parasuis. Danofloxacin AUC24h/MIC targets for the same endpoints for P. multocida were higher than those for H. parasuis. At the current dose of 2.5 mg/kg, the PK/PD cutoff (COPD) values of danofloxacin against P. multocida and H. parasuis were calculated to be 0.125 and 0.5 mg/L, respectively, based on Monte Carlo simulations. The predicted optimum doses of danofloxacin for a probability of target attainment (PTA) of > 90% to cover the overall MIC population distributions of P. multocida and H. parasuis in this study were 2.38 and 13.36 mg/kg, respectively. These PK/PD-based results have potential relevance for the clinical dose optimization and evaluation of susceptibility breakpoints for danofloxacin in the treatment of swine respiratory tract infections involving these pathogens.

Project description:BACKGROUND: The World Anti-Doping Agency (WADA) publishes the Prohibited List, a manually compiled international standard of substances and methods prohibited in-competition, out-of-competition and in particular sports. It would be ideal to be able to identify all substances that have one or more performance-enhancing pharmacological actions in an automated, fast and cost effective way. Here, we use experimental data derived from the ChEMBL database (~7,000,000 activity records for 1,300,000 compounds) to build a database model that takes into account both structure and experimental information, and use this database to predict both on-target and off-target interactions between these molecules and targets relevant to doping in sport. RESULTS: The ChEMBL database was screened and eight well populated categories of activities (Ki, Kd, EC50, ED50, activity, potency, inhibition and IC50) were used for a rule-based filtering process to define the labels "active" or "inactive". The "active" compounds for each of the ChEMBL families were thereby defined and these populated our bioactivity-based filtered families. A structure-based clustering step was subsequently performed in order to split families with more than one distinct chemical scaffold. This produced refined families, whose members share both a common chemical scaffold and bioactivity against a common target in ChEMBL. CONCLUSIONS: We have used the Parzen-Rosenblatt machine learning approach to test whether compounds in ChEMBL can be correctly predicted to belong to their appropriate refined families. Validation tests using the refined families gave a significant increase in predictivity compared with the filtered or with the original families. Out of 61,660 queries in our Monte Carlo cross-validation, belonging to 19,639 refined families, 41,300 (66.98%) had the parent family as the top prediction and 53,797 (87.25%) had the parent family in the top four hits. Having thus validated our approach, we used it to identify the protein targets associated with the WADA prohibited classes. For compounds where we do not have experimental data, we use their computed patterns of interaction with protein targets to make predictions of bioactivity. We hope that other groups will test these predictions experimentally in the future.

Dataset Information

Comparison of nine programs predicting pK(a) values of pharmaceutical substances.

Publications

Comparison of nine programs predicting pK(a) values of pharmaceutical substances.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets