Biomedical subjects

R Daniel Benz

Publications and source records attributed to R Daniel Benz.

11 recordsLinked to original sources

PubMed2007

A comprehensive model for reproductive and developmental toxicity hazard identification: I. Development of a weight of evidence QSAR database.

A weight of evidence (WOE) reproductive and developmental toxicology (reprotox) database was constructed that is suitable for quantitative structure-activity relationship (QSAR) modeling and human hazard identification of untested chemicals. The database was derived from multiple publicly available reprotox databases and consists of more than 10,000 individual rat, mouse, or rabbit reprotox tests linked to 2134 different organic chemical structures. The reprotox data were classified into seven general classes (male reproductive toxicity, female reproductive toxicity, fetal dysmorphogenesis, functional toxicity, mortality, growth, and newborn behavioral toxicity), and 90 specific categories as defined in the source reprotox databases. Each specific category contained over 500 chemicals, but the percentage of active chemicals was low, generally only 0.1-10%. The mathematical WOE model placed greater significance on confirmatory observations from repeat experiments, chemicals with multiple findings within a category, and the categorical relatedness of the findings. Using the weighted activity scores, statistical analyses were performed for specific data sets to identify clusters of categories that were correlated, containing similar profiles of active and inactive chemicals. The analysis revealed clusters of specific categories that contained chemicals that were active in two or more mammalian species (trans-species). Such chemicals are considered to have the highest potential risk to humans. In contrast, some specific categories exhibited only single species-specific activities. Results also showed that the rat and mouse were more susceptible to dysmorphogenesis than rabbits (6.1- and 3.6-fold, respectively).

Abnormalities, Drug-Induced↗

PubMed2006

A comprehensive model for reproductive and developmental toxicity hazard identification: II. Construction of QSAR models to predict activities of untested chemicals.

This report describes the construction, optimization and validation of a battery of quantitative structure-activity relationship (QSAR) models to predict reproductive and developmental (reprotox) hazards of untested chemicals. These models run with MC4PC software to predict seven general reprotox classes: male and female reproductive toxicity, fetal dysmorphogenesis, functional toxicity, mortality, growth, and newborn behavioral toxicity. The reprotox QSARs incorporate a weight of evidence paradigm using rats, mice, and rabbit reprotox study data and are designed to identify trans-species reprotoxicants. The majority of the reprotox QSARs exhibit good predictive performance properties: high specificity (>80%), low false positives (<20%), significant receiver operating characteristic (ROC) values (>2.00), and high coverage (>80%) in 10% leave-many-out validation experiments. The QSARs are based on 627-2023 chemicals and exhibited a wide applicability domain for FDA regulated organic chemicals for which they were designed. Experiments were also performed using the MC4PC multiple module prediction technology, and ROC statistics, and adjustments to the ratio of active to inactive (A/I ratio) chemicals in training data sets were made to optimize the predictive performance of QSAR models. Results revealed that an A/I ratio of approximately 40% was optimal for MC4PC. We discuss specific recommendations for the application of the reprotox QSAR battery.

Abnormalities, Drug-Induced↗

PubMed2006

Progress in QSAR toxicity screening of pharmaceutical impurities and other FDA regulated products.

Active ingredients in pharmaceutical products undergo extensive testing to ensure their safety before being made available to the American public. A consideration during the regulatory review process is the safety of pharmaceutical contaminants and degradents which may be present in the drug product at low levels. Several published guidances are available that outline the criteria for further testing of these impurities to assess their toxic potential, where further testing is in the form of a battery of toxicology assays and the identification of known structural alerts. However, recent advances in the development of computational methods have made available additional resources for safety assessment such as structure similarity searching and quantitative structure-activity relationship (QSAR) models. These methods offer a rapid and cost-effective first-pass screening capability to assess toxicity when conventional toxicology data are limited or lacking, with the potential to identify compounds that would be appropriate for further testing. This article discusses some of the considerations when using computational toxicology methods for regulatory decision support and gives examples of how the technology is currently being applied at the US Food and Drug Administration.

Animals↗

PubMed2006

Landscape of current toxicity databases and database standards.

Having readily available historical information for modeling toxicity has become important throughout the various stages of research and development. The high cost of late-phase attrition and recent international regulatory legislations have made even more acute the need to be able to mine the fragmented data and information available across diverse databases. In addition, the general trend to accelerate regulatory processes globally makes the effective use of existing data an imperative. To achieve efficient screening, develop profiles and gain the ability to cross reference, databases must be interoperated to allow data exchange and integration. Several database standards and controlled vocabulary initiatives have been used in the development of toxicity data models to transform the current landscape. This review describes the major databases of toxicological information now available, and provides a simple example of standardization that demonstrates the benefits of a toxicity database containing such qualified data.

Animals↗

PubMed2005

An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: I. Identification of carcinogens using surrogate endpoints.

A retrospective analysis of standard genetic toxicity (genetox) tests, reproductive and developmental toxicity (reprotox) studies, and rodent carcinogenicity bioassays (rcbioassay) was performed to identify the genetox and reprotox endpoints whose results best correlate with rcbioassay observations. A database of 7205 chemicals with genetox (n = 4961), reprotox (n = 2173), and rcbioassay (n = 1442) toxicity data was constructed; 1112 of the chemicals have both genetox and rcbioassay data and 721 chemicals have both reprotox and rcbioassay data. This study differed from previous studies by using conservative weight of evidence criteria to classify chemical carcinogens, data from 63 genetox and reprotox toxicological endpoints, and a new statistical parameter of correlation indicator (CI, the average of specificity and positive predictivity) to identify good surrogate endpoints for predicting carcinogenicity. Among 63 endpoints, results revealed that carcinogenicity was well correlated with certain tests for gene mutation (n = 8), in vivo clastogenicity (n = 2), unscheduled DNA synthesis assay (n = 1), and reprotox (n = 3). The current FDA regulatory battery of four genetox tests used to predict carcinogenicity includes two tests with good correlation (gene mutation in Salmonella and in vivo micronucleus) and two tests with poor correlation (mouse lymphoma gene mutation and in vitro chromosome aberrations) by our criteria.

Animals↗

PubMed2005

An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: II. Identification of genotoxicants, reprotoxicants, and carcinogens using in silico methods.

This study examined a novel method to identify carcinogens that employed expanded data sets composed of in silico data pooled with actual experimental genetic toxicity (genetox) and reproductive and developmental toxicity (reprotox) data. We constructed 21 modules using the MC4PC program including 13 of 14 (11 genetox and 3 reprotox) tests that we found correlated with results of rodent carcinogenicity bioassays (rcbioassays) [Matthews, E.J., Kruhlak, N.L., Cimino, M.C., Benz, R.D., Contrera, J.F., 2005b. An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: I. Identification of carcinogens using surrogate endpoints. Regul. Toxicol. Pharmacol.]. Each of the 21 modules was evaluated by cross-validation experiments and those with high specificity (SP) and positive predictivity (PPV) were used to predict activities of the 1442 chemicals tested for carcinogenicity for which actual genetox or reprotox data were missing. The expanded data sets had approximately 70% in silico data pooled with approximately 30% experimental data. Based upon SP and PPV, the expanded data sets showed good correlation with carcinogenicity testing results and had correlation indicator (CI, the average of SP and PPV) values of 75.5-88.7%. Conversely, expanded data sets for 9 non-correlated test endpoints were shown not to correlate with carcinogenicity results (CI values <75%). Results also showed that when Salmonella mutagenic carcinogens were removed from the 12 correlated, expanded data sets, only 7 endpoints showed added value by detecting significantly more additional carcinogens than non-carcinogens.

Animals↗

PubMed2005

In silico screening of chemicals for bacterial mutagenicity using electrotopological E-state indices and MDL QSAR software.

Quantitative structure-activity relationship (QSAR) software offers a rapid, cost effective means of prioritizing the mutagenic potential of chemicals. MDL QSAR models were developed using atom-type E-state indices and non-parametric discriminant analysis. Models were developed for Salmonella typhimurium gene mutation, combining results from strains TA97, TA98, TA100, TA1535, TA1536, TA1537, and TA1538 (n=3228), and Escherichia coli gene mutation tests WP2, WP100, and polA (n=472). Composite microbial mutation models (n=3338) were developed combining all Salmonella, E. coli, and the Bacillus subtilis rec spot test study results. The datasets contained 74% non-pharmaceuticals and 26% pharmaceuticals. Salmonella and microbial mutagenesis external validation studies included a total of 1444 and 1485 compounds, respectively. The average specificity, sensitivity, positive predictivity, concordance, and coverage of Salmonella models was 76, 81, 73, 78, and 98%, respectively, with similar performance for the microbial mutagenesis models. MDL QSAR and discriminant analysis provides rapid and highly automated mutagenicity screening software with good specificity, sensitivity, and coverage that is simpler and requires less user intervention than other similar software. MDL QSAR modules for microbial mutagenicity can provide efficient and cost effective large scale screening of compounds for mutagenic potential for the chemical and pharmaceutical industry.

Algorithms↗

PubMed2004

Estimating the safe starting dose in phase I clinical trials and no observed effect level based on QSAR modeling of the human maximum recommended daily dose.

Estimating the maximum recommended starting dose (MRSD) of a pharmaceutical for phase I human clinical trials and the no observed effect level (NOEL) for non-pharmaceuticals is currently based exclusively on an extrapolation of the results of animal toxicity studies. This process is inexact and requires the results of toxicity studies in multiple species (rat, dog, and monkey) to identify the no observed adverse effect level (NOAEL) and most sensitive test species. Multiple uncertainty (safety) factors are also necessary to compensate for incompatibility and uncertainty underlying the extrapolation of animal toxicity to humans. The maximum recommended daily dose for pharmaceuticals (MRDD) is empirically derived from human clinical trials. The MRDD is an estimated upper dose limit beyond which a drug's efficacy is not increased and/or undesirable adverse effects begin to outweigh beneficial effects. The MRDD is essentially equivalent to the NOAEL in humans, a dose beyond which adverse (toxicological) or undesirable pharmacological effects are observed. The NOAEL in test animals is currently used to estimate the safe starting dose in human clinical trials. MDL QSAR predictive modeling of the human MRDD may provide a better, simpler and more relevant estimation of the MRSD for pharmaceuticals and the toxic dose threshold of chemicals in humans than current animal extrapolation based risk assessment models and may be a useful addition to current methods. A database of the MRDD for over 1300 pharmaceuticals was compiled and modeled using MDL QSAR software and E-state and connectivity topological descriptors. MDL QSAR MRDD models were found to have good predictive performance with 74-78% of predicted MRDD values for 120 internal and 160 external validation compounds falling within a range of +/-10-fold the actual MRDD value. The predicted MRDD can be used to estimate the MRSD for pharmaceuticals in phase I clinical trials with the addition of a 10-fold safety factor. For non-pharmaceutical chemicals any compound-related effect can be considered an undesirable and adverse toxicological effect and the predicted MRDD can be used to estimate the NOEL with the addition of an appropriate safety factor.

Animals↗

PubMed2004

Assessment of the health effects of chemicals in humans: II. Construction of an adverse effects database for QSAR modeling.

The FDA's Spontaneous Reporting System (SRS) database contains over 1.5 million adverse drug reaction (ADR) reports for 8620 drugs/biologics that are listed for 1191 Coding Symbols for Thesaurus of Adverse Reaction (COSTAR) terms of adverse effects. We have linked the trade names of the drugs to 1861 generic names and retrieved molecular structures for each chemical to obtain a set of 1515 organic chemicals that are suitable for modeling with commercially available QSAR software packages. ADR report data for 631 of these compounds were extracted and pooled for the first five years that each drug was marketed. Patient exposure was estimated during this period using pharmaceutical shipping units obtained from IMS Health. Significant drug effects were identified using a Reporting Index (RI), where RI = (# ADR reports / # shipping units) x 1,000,000. MCASE/MC4PC software was used to identify the optimal conditions for defining a significant adverse effect finding. Results suggest that a significant effect in our database is characterized by > or = 4 ADR reports and > or = 20,000 shipping units during five years of marketing, and an RI > or = 4.0. Furthermore, for a test chemical to be evaluated as active it must contain a statistically significant molecular structural alert, called a decision alert, in two or more toxicologically related endpoints. We also report the use of a composite module, which pools observations from two or more toxicologically related COSTAR term endpoints to provide signal enhancement for detecting adverse effects.

Adverse Drug Reaction Reporting Systems↗

PubMed2004

Assessment of the health effects of chemicals in humans: I. QSAR estimation of the maximum recommended therapeutic dose (MRTD) and no effect level (NOEL) of organic chemicals based on clinical trial data.

The primary objective of this investigation was to develop a QSAR model to estimate the no effect level (NOEL) of chemicals in humans using data derived from pharmaceutical clinical trials and the MCASE software program. We believe that a NOEL model derived from human data provides a more specific estimate of the toxic dose threshold of chemicals in humans compared to current risk assessment models which extrapolate from animals to humans employing multiple uncertainty safety factors. A database of the maximum recommended therapeutic dose (MRTD) of marketed pharmaceuticals was compiled. Chemicals with low MRTDs were classified as high-toxicity compounds; chemicals with high MRTDs were classified as low-toxicity compounds. Two separate training data sets were constructed to identify specific structural alerts associated with high and low toxicity chemicals. A total of 134 decision alerts correlated with toxicity in humans were identified from 1309 training data set chemicals. An internal validation experiment showed that predictions for high- and low-toxicity chemicals were good (positive predictivity >92%) and differences between experimental and predicted MRTDs were small (0.27-0.70 log-fold). Furthermore, the model exhibited good coverage (89.9-93.6%) for three classes of chemicals (pharmaceuticals, direct food additives, and food contact substances). An additional investigation demonstrated that the maximum tolerated dose (MTD) of chemicals in rodents was poorly correlated with MRTD values in humans (R2 = 0.2005, n = 326). Finally, this report discusses experimental factors which influence the accuracy of test chemical predictions, potential applications of the model, and the advantages of this model over those that rely only on results of animal toxicology studies.

Animals↗

PubMed2003

Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices.

MDL QSAR (formerly SciVision QSAR IS) software is one of the several software systems under evaluation by the Informatics and Computational Safety Analysis Staff (ICSAS) of the FDA Center for Drug Evaluation and Research for regulatory and scientific decision support applications. MDL QSAR software contains an integrated set of tools for similarity searching, compound clustering, and modeling molecular structure related parameters that includes 240 electrotopological E-state, connectivity, and other descriptors. These molecular descriptors can be statistically correlated with toxicological or biological endpoints. The goal of this research was to evaluate the feasibility of using MDL QSAR software to develop structure-activity relationship (SAR) models that can be used to predict the carcinogenic potential of pharmaceuticals and organic chemicals. A validation study of 108 compounds that include 86 pharmaceuticals and 22 chemicals that were not present in a control rodent carcinogenicity data set of 1275 compounds demonstrated that MDL QSAR models had excellent coverage (93%) and good sensitivity (72%) and specificity (72%) for rodent carcinogenicity. The software correctly predicted 72% of non-carcinogenic compounds and compounds with carcinogenic findings. E-state descriptors contributed to more than half of the SAR models used to predict carcinogenic activity. We believe that electrotopological E-state descriptors and QSAR IS (MDL QSAR) software are promising new in silico approaches for modeling and predicting rodent carcinogenicity and may have application for other toxicological endpoints.

Algorithms↗