Next generation testing strategy for assessment of genomic damage: A conceptual framework and considerations.

For several decades, regulatory testing schemes for genetic damage have been standardized where the tests being utilized examined mutations and structural and numerical chromosomal damage. This has served the genetic toxicity community well when most of the substances being tested were amenable to such assays. The outcome from this testing is usually a dichotomous (yes/no) evaluation of test results, and in many instances, the information is only used to determine whether a substance has carcinogenic potential or not. Over the same time period, mechanisms and modes of action (MOAs) that elucidate a wider range of genomic damage involved in many adverse health outcomes have been recognized. In addition, a paradigm shift in applied genetic toxicology is moving the field toward a more quantitative dose-response analysis and point-of-departure (PoD) determination with a focus on risks to exposed humans. This is directing emphasis on genomic damage that is likely to induce changes associated with a variety of adverse health outcomes. This paradigm shift is moving the testing emphasis for genetic damage from a hazard identification only evaluation to a more comprehensive risk assessment approach that provides more insightful information for decision makers regarding the potential risk of genetic damage to exposed humans. To enable this broader context for examining genetic damage, a next generation testing strategy needs to take into account a broader, more flexible approach to testing, and ultimately modeling, of genomic damage as it relates to human exposure. This is consistent with the larger risk assessment context being used in regulatory decision making. As presented here, this flexible approach for examining genomic damage focuses on testing for relevant genomic effects that can be, as best as possible, associated with an adverse health effect. The most desired linkage for risk to humans would be changes in loci associated with human diseases, whether in somatic or germ cells. The outline of a flexible approach and associated considerations are presented in a series of nine steps, some of which can occur in parallel, which was developed through a collaborative effort by leading genetic toxicologists from academia, government, and industry through the International Life Sciences Institute (ILSI) Health and Environmental Sciences Institute (HESI) Genetic Toxicology Technical Committee (GTTC). The ultimate goal is to provide quantitative data to model the potential risk levels of substances, which induce genomic damage contributing to human adverse health outcomes. Any good risk assessment begins with asking the appropriate risk management questions in a planning and scoping effort. This step sets up the problem to be addressed (e.g., broadly, does genomic damage need to be addressed, and if so, how to proceed). The next two steps assemble what is known about the problem by building a knowledge base about the substance of concern and developing a rational biological argument for why testing for genomic damage is needed or not. By focusing on the risk management problem and potential genomic damage of concern, the next step of assay(s) selection takes place. The work-up of the problem during the earlier steps provides the insight to which assays would most likely produce the most meaningful data. This discussion does not detail the wide range of genomic damage tests available, but points to types of testing systems that can be very useful. Once the assays are performed and analyzed, the relevant data sets are selected for modeling potential risk. From this point on, the data are evaluated and modeled as they are for any other toxicology endpoint. Any observed genomic damage/effects (or genetic event(s)) can be modeled via a dose-response analysis and determination of an estimated PoD. When a quantitative risk analysis is needed for decision making, a parallel exposure assessment effort is performed (exposure assessment is not detailed here as this is not the focus of this discussion; guidelines for this assessment exist elsewhere). Then the PoD for genomic damage is used with the exposure information to develop risk estimations (e.g., using reference dose (RfD), margin of exposure (MOE) approaches) in a risk characterization and presented to risk managers for informing decision making. This approach is applicable now for incorporating genomic damage results into the decision-making process for assessing potential adverse outcomes in chemically exposed humans and is consistent with the ILSI HESI Risk Assessment in the 21st Century (RISK21) roadmap. This applies to any substance to which humans are exposed, including pharmaceuticals, agricultural products, food additives, and other chemicals. It is time for regulatory bodies to incorporate the broader knowledge and insights provided by genomic damage results into the assessments of risk to more fully understand the potential of adverse outcomes in chemically exposed humans, thus improving the assessment of risk due to genomic damage. The historical use of genomic damage data as a yes/no gateway for possible cancer risk has been too narrowly focused in risk assessment. The recent advances in assaying for and understanding genomic damage, including eventually epigenetic alterations, obviously add a greater wealth of information for determining potential risk to humans. Regulatory bodies need to embrace this paradigm shift from hazard identification to quantitative analysis and to incorporate the wider range of genomic damage in their assessments of risk to humans. The quantitative analyses and methodologies discussed here can be readily applied to genomic damage testing results now. Indeed, with the passage of the recent update to the Toxic Substances Control Act (TSCA) in the US, the new generation testing strategy for genomic damage described here provides a regulatory agency (here the US Environmental Protection Agency (EPA), but suitable for others) a golden opportunity to reexamine the way it addresses risk-based genomic damage testing (including hazard identification and exposure). Environ. Mol. Mutagen. 58:264-283, 2017. © 2016 The Authors. Environmental and Molecular Mutagenesis Published by Wiley Periodicals, Inc.

Genetic toxicology in the 21st century: reflections and future directions.

A symposium at the 40th anniversary of the Environmental Mutagen Society, held from October 24-28, 2009 in St. Louis, MO, surveyed the current status and future directions of genetic toxicology. This article summarizes the presentations and provides a perspective on the future. An abbreviated history is presented, highlighting the current standard battery of genotoxicity assays and persistent challenges. Application of computational toxicology to safety testing within a regulatory setting is discussed as a means for reducing the need for animal testing and human clinical trials, and current approaches and applications of in silico genotoxicity screening approaches across the pharmaceutical industry were surveyed and are reported here. The expanded use of toxicogenomics to illuminate mechanisms and bridge genotoxicity and carcinogenicity, and new public efforts to use high-throughput screening technologies to address lack of toxicity evaluation for the backlog of thousands of industrial chemicals in the environment are detailed. The Tox21 project involves coordinated efforts of four U.S. Government regulatory/research entities to use new and innovative assays to characterize key steps in toxicity pathways, including genotoxic and nongenotoxic mechanisms for carcinogenesis. Progress to date, highlighting preliminary test results from the National Toxicology Program is summarized. Finally, an overview is presented of ToxCast™, a related research program of the U.S. Environmental Protection Agency, using a broad array of high throughput and high content technologies for toxicity profiling of environmental chemicals, and computational toxicology modeling. Progress and challenges, including the pressing need to incorporate metabolic activation capability, are summarized.

New and emerging technologies for genetic toxicity testing.

The International Life Sciences Institute (ILSI) Health and Environmental Sciences Institute (HESI) Project Committee on the Relevance and Follow-up of Positive Results in In Vitro Genetic Toxicity (IVGT) Testing established an Emerging Technologies and New Strategies Workgroup to review the current State of the Art in genetic toxicology testing. The aim of the workgroup was to identify promising technologies that will improve genotoxicity testing and assessment of in vivo hazard and risk, and that have the potential to help meet the objectives of the IVGT. As part of this initiative, HESI convened a workshop in Washington, DC in May 2008 to discuss mature, maturing, and emerging technologies in genetic toxicology. This article collates the abstracts of the New and Emerging Technologies Workshop together with some additional technologies subsequently considered by the workgroup. Each abstract (available in the online version of the article) includes a section addressed specifically to the strengths, weaknesses, opportunities, and threats associated with the respective technology. Importantly, an overview of the technologies and an indication of how their use might be aligned with the objectives of IVGT are presented. In particular, consideration was given with regard to follow-up testing of positive results in the standard IVGT tests (i.e., Salmonella Ames test, chromosome aberration assay, and mouse lymphoma assay) to add weight of evidence and/or provide mechanism of action for improved genetic toxicity risk assessments in humans.

Testing computational toxicology models with phytochemicals.

Computational toxicology employing quantitative structure-activity relationship (QSAR) modeling is an evidence-based predictive method being evaluated by regulatory agencies for risk assessment and scientific decision support for toxicological endpoints of interest such as rodent carcinogenicity. Computational toxicology is being tested for its usefulness to support the safety assessment of drug-related substances (e.g. active pharmaceutical ingredients, metabolites, impurities), indirect food additives, and other applied uses of value for protecting public health including safety assessment of environmental chemicals. The specific use of QSAR as a chemoinformatic tool for estimating the rodent carcinogenic potential of phytochemicals present in botanicals, herbs, and natural dietary sources is investigated here by an external validation study, which is the most stringent scientific method of measuring predictive performance. The external validation statistics for predicting rodent carcinogenicity of 43 phytochemicals, using two computational software programs evaluated at the FDA, are discussed. One software program showed very good performance for predicting non-carcinogens (high specificity), but both exhibited poor performance in predicting carcinogens (sensitivity), which is consistent with the design of the models. When predictions were considered in combination with each other rather than based on any one software, the performance for sensitivity was enhanced, However, Chi-square values indicated that the overall predictive performance decreases when using the two computational programs with this particular data set. This study suggests that complementary multiple computational toxicology software need to be carefully selected to improve global QSAR predictions for this complex toxicological endpoint.

Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans. Part A: use of FDA post-market reports to create a database of hepatobiliary and urinary tract toxicities.

The Informatics and Computational Safety Analysis Staff at the US FDA's Center for Drug Evaluation and Research has created a database of pharmaceutical adverse effects (AEs) linked to pharmaceutical chemical structures and estimated population exposures. The database is being used to develop quantitative structure-activity relationship (QSAR) models for the prediction of drug-induced liver and renal injury, as well as to identify relationships among AEs. The post-market observations contained in the database were obtained from FDA's Spontaneous Reporting System (SRS) and the Adverse Event Reporting System (AERS) accessed through Elsevier PharmaPendium software. The database contains approximately 3100 unique pharmaceutical compounds and 9685 AE endpoints. To account for variations in AE reports due to different patient populations and exposures for each drug, a proportional reporting ratio (PRR) was used. The PRR was applied to all AEs to identify chemicals that could be scored as positive in the training datasets of QSAR models. Additionally, toxicologically similar AEs were grouped into clusters based upon both biological effects and statistical correlation. This clustering created a weight of evidence paradigm for the identification of compounds most likely to cause human harm based upon findings in multiple related AE endpoints.

Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans: Part B. Use of (Q)SAR systems for early detection of drug-induced hepatobiliary and urinary tract toxicities.

This report describes the development of quantitative structure-activity relationship (QSAR) models for predicting rare drug-induced liver and urinary tract injury in humans based upon a database of post-marketing adverse effects (AEs) linked to approximately 1600 chemical structures. The models are based upon estimated population exposure using AE proportional reporting ratios. Models were constructed for 5 types of liver injury (liver enzyme disorders, cytotoxic injury, cholestasis and jaundice, bile duct disorders, gall bladder disorders) and 6 types of urinary tract injury (acute renal disorders, nephropathies, bladder disorders, kidney function tests, blood in urine, urolithiases). Identical training data sets were configured for 4 QSAR programs (MC4PC, MDL-QSAR, BioEpisteme, and Predictive Data Miner). Model performance was optimized and was shown to be affected by the AE scoring method and the ratio of the number of active to inactive drugs. The best QSAR models exhibited an overall average 92.4% coverage, 86.5% specificity and 39.3% sensitivity. The 4 QSAR programs were demonstrated to be complementary and enhanced performance was obtained by combining predictions from 2 programs (average 78.4% specificity, 56.2% sensitivity). Consensus predictions resulted in better performance as judged by both internal and external validation experiments.

Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans: Part C: use of QSAR and an expert system for the estimation of the mechanism of action of drug-induced hepatobiliary and urinary tract toxicities.

This report describes an in silico methodology to predict off-target pharmacologic activities and plausible mechanisms of action (MOAs) associated with serious and unexpected hepatobiliary and urinary tract adverse effects in human patients. The investigation used a database of 8,316,673 adverse event (AE) reports observed after drugs had been marketed and AEs noted in the published literature that were linked to 2124 chemical structures and 1851 approved clinical indications. The Integrity database of drug patent and literature studies was used to find pharmacologic targets and proposed clinical indications. BioEpisteme QSAR software was used to predict possible molecular targets of drug molecules and Derek for Windows expert system software to predict chemical structural alerts and plausible MOAs for the AEs. AEs were clustered into five types of liver injury: liver enzyme disorders, cytotoxic injury, cholestasis and jaundice, bile duct disorders, and gall bladder disorders, and six types of urinary tract injury: acute renal disorders, nephropathies, bladder disorders, kidney function tests, blood in urine, and urolithiasis. Results showed that drug-related AEs were highly correlated with: (1) known drug class warnings, (2) predicted off-target activities of the drugs, and (3) a specific subset of clinical indications for which the drug may or may not have been prescribed.

Combined Use of MC4PC, MDL-QSAR, BioEpisteme, Leadscope PDM, and Derek for Windows Software to Achieve High-Performance, High-Confidence, Mode of Action-Based Predictions of Chemical Carcinogenesis in Rodents.

ABSTRACT This report describes a coordinated use of four quantitative structure-activity relationship (QSAR) programs and an expert knowledge base system to predict the occurrence and the mode of action of chemical carcinogenesis in rodents. QSAR models were based upon a weight-of-evidence paradigm of carcinogenic activity that was linked to chemical structures (n = 1,572). Identical training data sets were configured for four QSAR programs (MC4PC, MDL-QSAR, BioEpisteme, and Leadscope PDM), and QSAR models were constructed for the male rat, female rat, composite rat, male mouse, female mouse, composite mouse, and rodent composite endpoints. Model predictions were adjusted to favor high specificity (>80%). Performance was shown to be affected by the method used to score carcinogenicity study findings and the ratio of the number of active to inactive chemicals in the QSAR training data set. Results demonstrated that the four QSAR programs were complementary, each detecting different profiles of carcinogens. Accepting any positive prediction from two programs showed better overall performance than either of the single programs alone; specificity, sensitivity, and Chi-square values were 72.9%, 65.9%, and 223, respectively, compared to 84.5%, 45.8%, and 151. Accepting only consensus-positive predictions using any two programs had the best overall performance and higher confidence; specificity, sensitivity, and Chi-square values were 85.3%, 57.5%, and 287, respectively. Specific examples are provided to demonstrate that consensus-positive predictions of carcinogenicity by two QSAR programs identified both genotoxic and nongenotoxic carcinogens and that they detected 98.7% of the carcinogens linked in this study to Derek for Windows defined modes of action.

In Silico Screening of Chemicals for Genetic Toxicity Using MDL-QSAR, Nonparametric Discriminant Analysis, E-State, Connectivity, and Molecular Property Descriptors.

ABSTRACT Genetic toxicity testing is a critical parameter in the safety assessment of pharmaceuticals, food constituents, and environmental and industrial chemicals. Quantitative structure-activity relationship (QSAR) software offers a rapid, cost-effective means of prioritizing the genotoxic potential of chemicals. Our goal is to develop and validate a complete battery of complementary QSAR models for genetic toxicity. We previously reported the development of MDL-QSAR models for the prediction of mutations in Salmonella typhimurium and Escherichia coli ( Contrera et al. 2005b ); this report describes the development of eight additional models for mutagenicity, clastogenicity, and DNA damage. The models were created using MDL-QSAR atom-type E-state, simple connectivity and molecular property descriptor categories, and nonparametric discriminant analysis. In 10% leave-group-out internal validation studies, the specificity of the models ranged from 63% for the mouse lymphoma (L5178Y-tk) model to 88% for chromosome aberrations in vivo. Sensitivity ranged from a high of 74% for the mouse lymphoma model to a low of 39% for the unscheduled DNA synthesis model. The receiver operator characteristic (ROC) was >/=2.00, a value indicative of good predictive performance. The predictive performance of MDL-QSAR models was also shown to compare favorably to the results of MultiCase MC4PC ( Matthews et al. 2006b ) genotoxicity models prepared with the same training data sets. MDL-QSAR software models exhibit good specificity, sensitivity, and coverage and they can provide rapid and cost-effective large-scale screening of compounds for genotoxic potential by the chemical and pharmaceutical industry and for regulatory decision support applications.

Comparison of MC4PC and MDL-QSAR rodent carcinogenicity predictions and the enhancement of predictive performance by combining QSAR models.

This report presents a comparison of the predictive performance of MC4PC and MDL-QSAR software as well as a method for combining the predictions from both programs to increase overall accuracy. The conclusions are based on 10 x 10% leave-many-out internal cross-validation studies using 1540 training set compounds with 2-year rodent carcinogenicity findings. The models were generated using the same weight of evidence scoring method previously developed [Matthews, E.J., Contrera, J.F., 1998. A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software. Regul. Toxicol. Pharmacol. 28, 242-264.]. Although MC4PC and MDL-QSAR use different algorithms, their overall predictive performance was remarkably similar. Respectively, the sensitivity of MC4PC and MDL-QSAR was 61 and 63%, specificity was 71 and 75%, and concordance was 66 and 69%. Coverage for both programs was over 95% and receiver operator characteristic (ROC) intercept statistic values were above 2.00. The software programs had complimentary coverage with none of the 1540 compounds being uncovered by both MC4PC and MDL-QSAR. Merging MC4PC and MDL-QSAR predictions improved the overall predictive performance. Consensus sensitivity increased to 67%, specificity to 84%, concordance to 76%, and ROC to 4.31. Consensus rules can be tuned to reflect the priorities of the user, so that greater emphasis may be placed on predictions with high sensitivity/low false negative rates or high specificity/low false positive rates. Sensitivity was optimized to 75% by reclassifying all compounds predicted to be positive in MC4PC or MDL-QSAR as positive, and specificity was optimized to 89% by reclassifying all compounds predicted negative in MC4PC or MDL-QSAR as negative.

Toxicological and clinical computational analysis and the US FDA/CDER.

In this article the author attempts to introduce those not familiar with computational toxicology to some of the terminology and basic principles of the field. The author then reports on the progress that the FDA, Center for Drug Evaluation and Research has made in compiling databases of toxicological and clinical data from which successful predictive toxicology models have been made, many of which are now commercially available through FDA software developer collaborators. This report is concluded with the author's personal speculations on the future of computational toxicology in general, and at US FDA in particular.

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.

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.

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.

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.

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.

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.

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.

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.