Orest T. Macina

Development, Characterization and Application of Predictive-toxicology Models

The adoption of SAR techniques for risk assessment purposes requires that the predictive performance of models be characterized and optimized. The development of such methods with respect to CASE/MULTICASE are described. Moreover, the effects of size, informational content, ratio of actives/inactives in the model on predictivity must be determined. Characterized models can provide mechanistic insights: nature of toxicophore, reactivity, receptor binding. Comparison of toxicophores among SAR models allows a determination of mechanistic overlaps (e.g., mutagenicity, toxicity, inhibition of gap junctional intercellular communication vs. carcinogenicity). Methods have been developed to combine SAR submodels and thereby improve predictive performance. Now that predictive toxicology methods are gaining acceptance, the development of Good Laboratory Practices is a further priority, as is the development of graduate programs in Computational Toxicology to adequately train the needed professional.

Computer automated structure evaluation of quinolone antibacterial agents.

The Computer Automated Structure Evaluation (CASE) program was used to study a series of quinolone antibacterial agents for which experimental data pertaining to DNA gyrase inhibition as well as MICs against several strains of gram-positive and gram-negative bacteria are available. The result of the analysis was the automatic generation of molecular fragments relevant to the respective biological endpoints. The potential significance of these major activating-inactivating fragments to the biological activity is discussed.

Structure-activity relationships and computer-assisted analysis of respiratory sensitization potential

The mechanism(s) underlying respiratory sensitivity to chemicals is uncertain but is assumed to involve immunologic components with pharmacologic and neurologic involvement. Predictive testing would be valuable to prevent occurrence of hypersensitivity. Several in vitro and in vivo approaches have been used for predictive purposes. In vitro methods have included assessment of the ability of the chemical to undergo reaction with proteins. Computational methods have investigated the relationship between structure and electrophilic potential of chemical allergens. We have initiated a structure-activity evaluation of chemicals associated with elicitation of respiratory sensitization and have utilized a computer-based expert system, MultiCASE. A preliminary database of 39 active chemicals has been established from a literature search of clinical case reports and animal test results. Evaluation of the model has indicated structural alerts for activity which consist of structural fragments as well as physicochemical properties. Further development of the model and evaluation of findings should enable mechanistic insight into the process of respiratory sensitization and recognition of factors which distinguish respiratory sensitizers mechanistically from other chemical allergens such as contact sensitizing chemicals.

Structural determinants associated with risk of human developmental toxicity

OBJECTIVES Identifying drugs or chemicals that represent hazards to human development is a continuous challenge. Of the approximately 60,000 chemicals in commercial use only 5% have been evaluated for developmental toxicity. Identification of inexpensive, rapid, validated techniques to demonstrate chemical hazards for the human embryo or fetus is the objective of this research. STUDY DESIGN This research explored identification of structure activity predictors associated with human developmental toxicity by means of MULTICASE (multiple computer-automated structure evaluation), an algorithm that evaluates associations between chemicals and their constituent fragments and a biologic response. This algorithm allows identification of chemicals (and specific substructures) that may be human developmental toxicants. Developmental toxicity data were compiled from two sources (the Teratogen Information System and Food and Drug Administration guidelines) and analyzed to identify structural determinants (biophores) associated with human developmental toxicity. RESULTS This analysis identified 17 biophores associated with human developmental toxicity. Testing the biophores against the learning set demonstrated 99% concordance, 100% sensitivity, and 98% specificity. Cross-validation studies were conducted, in which the original database was randomly separated into five learning and test sets; these demonstrated a mean concordance of 73%, with a mean sensitivity of 63% and a mean specificity of 79%. CONCLUSIONS The MULTICASE structure-activity model is useful for identifying potential human developmental toxicants, as well as serving as a starting point for mechanistic investigations.

Use of the Computer Automated Structure Evaluation program in determining quantitative structure-activity relationships within hallucinogenic phenylalkylamines.

The Computer Automated Structure Evaluation (CASE) program has been successfully used to generate automatically and identify molecular fragments relevant to the hallucinogenic activity expressed by some phenylalkylamines. Utilizing these major fragments, Quantitative Structure-Activity Relationship (QSAR) calculations were carried out to obtain an equation which was used for predictions of potencies. Correlations of these major activating/inactivating fragments with the biological activity of the compounds, as well as predictive capabilities of the CASE program, are discussed.

Structural determinants of developmental toxicity in hamsters.

A CASE/MULTICASE structure activity relationship (SAR) model of developmental toxicity of chemicals in hamsters (HaDT) was developed. The model exhibited a predictive performance of 74%. The models overall predictivity and informational content were similar to those of an SAR model of mutagenicity in Salmonella. However, unlike the Salmonella mutagenicity model, the HaDT model did not identify overtly chemically reactive moieties as associated with activity. Moreover, examination of the number and nature of significant structural determinants suggested that developmental toxicity in hamsters was not the result of a unique mechanism or attack on a specific molecular target. The analysis also indicated that the availability of experimental data on additional chemicals would improve the performance of the SAR model.

COMPUTER AIDED OLIVE OIL-GAS PARTITION COEFFICIENT CALCULATIONS

Our project aimed at developing a relation between the structure of diverse organic molecules and their ability to partition between olive oil and gas. Olive oil−gas partitions have been used as surrogates for blood−gas partition, which is believed to play a role in the pharmacokinetic profile of organic solvents. We found that linear relationships exist between olive oil−gas (Loil) and hexadecane−gas (Lhex) partition coefficients within certain chemical classes. Log Loil models were also derived from a multivariate regression analysis based on log Loil values of organic molecules and a set of basic functional groups and correction factors found previously to be useful in the calculation of log P. Both cross-validation experiments and the calculation of an independent set of 36 compounds demonstrated that our methodology can give fast and accurate log Loil predictions for the classes of compounds represented within the models. Molecular weight was found to be an important factor in the determination of lo...

STRUCTURAL BASIS OF SENSORY IRRITATION

MultiCASE (version 2.80), a structure-activity relationship expert system, was applied to a series of structurally diverse compounds evaluated for sensory irritant properties. The resulting biophores (molecular fragments responsible for the observed activity) were interpreted in terms of their physicochemical properties. Aromatic, charge-charge, and lipophilic interactions with a hypothetical receptor site are proposed. Fragment overlaps were observed with other databases of toxicological interest. The MultiCASE model is useful for predictive purposes and for providing information of a mechanistic nature. Furthermore, the identified biophores can serve as starting points for in-depth exploration of the correlation between physicochemical properties and biological activity.

Computer automated structure evaluation of opiate alkaloids

Abstract The Computer Automated Structure Evaluation (CASE) program has been applied to the evaluation of mu-receptor binding for a diverse group of opiate alkaloids. Major molecular fragments relevant to the drug-receptor interaction were automatically generated and incorporated within an equation used to estimate the degree of binding. Correlations of these major activating/inactivating fragments with biological activity are discussed.

Evaluating clinical case report data for SAR modeling of allergic contact dermatitis

Clinical case reports can be important sources of information for alerting health professionals to the existence of possible health hazards. Isolated case reports, however, are weak evidence of causal relationships between exposure and disease because they do not provide an indication of the frequency of a particular exposure leading to a disease event. A database of chemicals causing allergic contact dermatitis (ACD) was compiled to discern structure-activity relationships. Clinical reports repre sented a considerable fraction of the data. Multiple Computer Automated Structure Evaluation (MultiCASE) was used to create a structure-activity model to be used in predicting the ACD activity of untested chemicals. We examined how the predictive ability of the model was influenced by including the case report data in the model. In addition, the model was used to predict the activity of chemicals identified from clinical case reports. The following results were obtained: • When chemicals which were identified as dermal sensitizers by only one or two case reports were included in the model, the specificity of the model was reduced. • Less than one half of these chemicals were predicted to be active by the most highly evidenced model. • These chemicals possessed substructures not pre viously encountered by any of the models. We conclude that chemicals classified as sensitizers based on isolated clinical case reports be excluded from our model of ACD. The approach described here for evaluating activity of chemicals based on sparse evidence should be considered for use with other endpoints of toxicity when data are correspondingly limited.