In vitro screening and chemometrics analysis on a series of azole derivatives with fungicide activity against moniliophthora perniciosa

Moniliophthora perniciosa, the causal agent of witches' broom disease in Theobroma cacao, significantly decreased cacao production, especially in Bahia State, the largest cocoa producing of the American continent. Control programs developed so far have low efficiency. Azole derivatives are active both in vitro and in loco against M. perniciosa, however there is no comprehensive study on the activity of azoles against this phytopatogen. Standardized in vitro biological data were employed to develop supervised and unsupervised chemometric models that highlight physicochemical and structural features that are crucial for azole's fungicidal activity against M. perniciosa. Thus, PCA and SIMCA models suggest that electronegativity (BEHe3) and dipolar moment (JGI4), as well as H-bonding to M. pernciosa's lanosterol 14α-desmethylase active site and lack of Cl atoms 6 to 8 bonds from the azole's nitrogen atoms play a major role to azoles' fungicide activity

Pharmacophore derivation using discotech and comparison of semi-emperical, AB initio and density functional CoMFA studies for sigma 1 and sigma 2 receptor-ligands

This study describes the development of pharmacophore and CoMFA models for sigma receptor ligands. CoMFA studies were performed for 48 bioactive sigma 1 receptorligands using [H3 ](+) pentazocine as the radioligand, for 30 PCP derivatives for sigma 1 receptor-ligands using [3H](+)SK-F 10047 as the radioligand and for 24 bioactive sigma 2 receptor-ligands using the radioligand [H3](+)DTG in the presence of pentazocine. Distance Comparisons (DISCOtech) was used as the starting point for CoMFA studies. The conformers, derived by DISCOtech were optimized using AMi, or HF/3-21G* in Gaussian 98. The optimized geometries were aligned with the pharmacophore, derived using DISCOtech. Atomic charges were calculated using AMl, HF/3-21G*, B3LYP/3-21G*, MP2/3-21G* methods in Gaussian 98. The CoMFA Maps that were developed using Sybyl 6.9 were compared on steric and electrostatic field differences. With leaveone-out cross validation the numbers of optimal components were decided. Using these numbers of optimal components no cross validation was performed in a training set. After a test set, it was known that CoMFA models derived from HF/3-21G* optimized geometries were more reliable in predicting bioactivities than CoMFA models derived from AMi optimized geometries

Review of QSAR Models and Software Tools for predicting Biokinetic Properties

In the assessment of industrial chemicals, cosmetic ingredients, and active substances in pesticides and biocides, metabolites and degradates are rarely tested for their toxicologcal effects in mammals. In the interests of animal welfare and cost-effectiveness, alternatives to animal testing are needed in the evaluation of these types of chemicals. In this report we review the current status of various types of in silico estimation methods for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their metabolites/degradation products. The review was performed in a broad sense, with emphasis on QSARs and rule-based approaches and their applicability to estimation of oral bioavailability, human intestinal absorption, blood-brain barrier penetration, plasma protein binding, metabolism and. This revealed a vast and rapidly growing literature and a range of software tools. While it is difficult to give firm conclusions on the applicability of such tools, it is clear that many have been developed with pharmaceutical applications in mind, and as such may not be applicable to other types of chemicals (this would require further research investigation). On the other hand, a range of predictive methodologies have been explored and found promising, so there is merit in pursuing their applicability in the assessment of other types of chemicals and products. Many of the software tools are not transparent in terms of their predictive algorithms or underlying datasets. However, the literature identifies a set of commonly used descriptors that have been found useful in ADME prediction, so further research and model development activities could be based on such studies.JRC.DG.I.6-Systems toxicolog

Applying Linear and Non-Linear Methods for Parallel Prediction of Volume of Distribution and Fraction of Unbound Drug

Volume of distribution and fraction unbound are two key parameters in pharmacokinetics. The fraction unbound describes the portion of free drug in plasma that may extravasate, while volume of distribution describes the tissue access and binding of a drug. Reliable in silicopredictions of these pharmacokinetic parameters would benefit the early stages of drug discovery, as experimental measuring is not feasible for screening purposes. We have applied linear and nonlinear multivariate approaches to predict these parameters: linear partial least square regression and non-linear recursive partitioning classification. The volume of distribution and fraction of unbound drug in plasma are predicted in parallel within the model, since the two are expected to be affected by similar physicochemical drug properties. Predictive models for both parameters were built and the performance of the linear models compared to models included in the commercial software Volsurf+. Our models performed better in predicting the unbound fraction (Q2 0.54 for test set compared to 0.38 with Volsurf+ model), but prediction accuracy of the volume of distribution was comparable to the Volsurf+ model (Q2 of 0.70 for test set compared to 0.71 with Volsurf+ model). The nonlinear classification models were able to identify compounds with a high or low volume of distribution (sensitivity 0.81 and 0.71, respectively, for test set), while classification of fraction unbound was less successful. The interrelationship between the volume of distribution and fraction unbound is investigated and described in terms of physicochemical descriptors. Lipophilicity and solubility descriptors were found to have a high influence on both volume of distribution and fraction unbound, but with an inverse relationship.Peer reviewe

HLA class I supertype and supermotif definition by chemometric approaches.

Activation of cytotoxic T cells in human requires specific binding of antigenic peptides to human leukocyte antigen (HLA) molecules. HLA is the most polymorphic protein in the human body, currently 1814 different alleles collected in the HLA sequence database at the European Bioinformatics Institute. Most of the HLA molecules recognise different peptides. Also, some peptides can be recognised by several of HLA molecules. In the present project, all available class I HLA alleles are classified into supertypes. Super - binding motifs for peptides binding to some supertypes are defined where binding data are available. A variety of chemometric techniques are used in the project, including 2D and 3D QSAR techniques and different variable selection methods like SIMCA, GOLPE and genetic algorithm. Principal component analysis combined with molecular interaction fields calculation by the program GRID is used in the class I HLA classification. This thesis defines an HLA-A3 supermotif using two QSAR methods: the 3D-QSAR method CoMSIA, and a recently developed 2D-QSAR method, which is named the additive method. Four alleles with high phenotype frequency were included in the study: HLA-A*0301, HLA-A*1101, HLA-A*3101 and HLA- A*6801. An A*020T binding motif is also defined using amino acid descriptors and variable selection methods. Novel peptides have been designed according to the motifs and the binding affinity is tested experimentally. The results of the additive method are used in the online server, MHCPred, to predict binding affinity of unknown peptides. In HLA classification, the HLA-A, B and C molecules are classified into supertypes separately. A total of eight supertypes are observed for class I HLA, including A2, A3, A24, B7, B27, B44, CI and C4 supertype. Using the HLA classification, any newly discovered class I HLA molecule can be grouped into a supertype easily, thus simplifying the experimental function characterisation process

Ocular and systemic pharmacokinetic models for drug discovery and development

Drug discovery and development is a long process: it takes usually 12 to 15 years before a drug candidate reaches the market. The pharmacokinetics of the drug is an important aspect of drug discovery and development, because the drug must reach its target site and exert the therapeutic response. The pharmacokinetic parameters of new compounds should be investigated early in drug discovery. Pharmacokinetic predictions can be made with Quantitative Structure-Property Relationships (QSPR) which are computational models that correlate chemical features with pharmacokinetic properties. The correlations are based on in vivo or in vitro pharmacokinetic data and molecular descriptors. QSPR models can be used to predict the pharmacokinetic parameters even before any actual drug synthesis and can be exploited to guide drug discovery. Pharmacokinetic models can also simulate concentration profiles of drugs during the drug discovery and development process. It was decided to develop QSPR models of pharmacokinetic parameters of drugs to be delivered by the systemic or ocular routes. A combination of Principal Component Analysis and Partial Least Square multivariate statistical methods was used to obtain QSPR equations for volume of drug distribution and fraction of unbound drug in plasma. Parallel modelling of these parameters resulted in acceptable R2 (0.58 - 0.77) and Q2 values (0.55 - 0.58). These models are based on a large set of structurally unrelated compounds, they are open and they have a defined applicability domain. Charge and lipophilicity related descriptors were the relevant ones which influenced the volume of distribution and free fraction of drug in plasma. Pharmacokinetics is an important factor in the development of ocular medications, because the ocular drug targets are difficult to reach, particularly in the posterior tissues such as retina and choroid. Therefore, drugs need to be injected intravitreally in the treatment of retina and choroid diseases (e.g. in exudative age-related macular degeneration) and thus prediction of intravitreal pharmacokinetics would be especially advantageous in ocular drug discovery and development. The first comprehensive collection of intravitreal volume of distribution and clearance values of compounds was collated based on extensive rabbit eye data from the literature. Moreover, predictive QSPR models for intravitreal clearance and half-life were created which had R2 and Q2 values of 0.62 0.84 for clearance and 0.61 - 0.80 for half-life. LogD7.4 and hydrogen bonding capacity defined the intravitreal clearance and half-life of compounds with a molecular weight below 1500 Da. The intravitreal volumes of drug distribution lay within a narrow range (80% within 1.18 - 2.28 ml). The QSPR models for intravitreal clearance and the typical values for intravitreal volumes of distribution were implemented in pharmacokinetic simulation models; the simulated profiles based on the real and predicted pharmacokinetic parameter values were similar. Thus, a combination of QSPR and pharmacokinetic models can be used in drug discovery and development to aid in the design of drugs and drug delivery systems. A comprehensive comparison of intravitreal pharmacokinetic data between rabbit and human was carried out to clarify the translational value of the rabbit model. The analysis revealed that the rabbit can be considered as a clinically predictive animal model for intravitreal pharmacokinetics of small molecules (18 Da - 1500 Da) and macromolecules (7.1 kDa - 149 kDa). There was a correlation between the intravitreal clearance values in human patients and healthy rabbits; they showed similar, but not identical, absolute values. The intravitreal pharmacokinetics of small molecules is mainly governed by permeability-limited clearance across blood-ocular barriers and occurs via the posterior route, whereas large molecules are cleared mostly via the anterior route. Although the literature contains some claims about the significance of the viscosity of the vitreous, it seems that this is not a major factor in drug elimination from the eye. In conclusion, new in silico tools were generated for systemic and ocular pharmacokinetics and drug delivery. These models can be exploited in industrial drug discovery and will hopefully speed up the development of new medications.Silmätaudeissa lääkehoitoa vaikeuttaa se, että lääkehoitoa on vaikea saattaa perille silmänpohjaan verkkokalvon soluihin, joissa näkövammaisuuteen ja sokeutumiseen johtavat muutokset tavallisesti tapahtuvat. Näin ollen lääkkeitä joudutaan antamaan silmän sisään toistuvina injektioina esimerkiksi verkkokalvon ikärappeuman hoidossa. Lääkkeiden kulkeutumisen ymmärtäminen ja ennustaminen tietokoneella auttaa pitkävaikutteisten injektioiden ja vaihtoehtoisten lääkkeen antotapojen kehittämistä. Väitöskirjassa kehitettiin tällaisia tietokonemalleja pohjautuen julkaistuihin tutkimuksiin

Design and synthesis of cysmethynil and analogues as inhibitors of isoprenylcysteine carboxyl methyltransferase (ICMT)

Ph.DDOCTOR OF PHILOSOPH