A Systems Approach to Alternatives for Toxicity Testing. George Daston

Transcription

1 A Systems Approach to Alternatives for Toxicity Testing George Daston

2 Toxicology: From an Empirical to a Predictive Science O HO Traditional Approach (Black box): Use a model that we have (some) confidence in, but incomplete understanding of how it works Desired Approach: Predictions based on deep, fundamental understanding HO N

3 Changes in Safety Approaches, In vivo testing Changing data streams Changing technology In vitro testing Computational methods Models and simulations

4 Systems Toxicology at P&G Adds foundational capabilities to, and learns from, SB platform Cheminformatics - computational approach to identify analogs Modeling weak chem-biological interactions - a key to understanding toxicity and efficacy at a non-rx level Application of microarray data for rapid prediction of toxicity Dynamic model of local effects - adds a kinetic component essential for epithelial biology, Beauty Technology Dynamic models of systemic toxicity - a rational approach to non-animal evaluation of organ toxicity

5 Cheminformatics: Alternatives to Alternatives Substructure searching Genotoxicity (19,300) Carcinogenicity (15,800) Skin Sensitization (9,400) Skin Irritation (10,400) Reproductive/Developmental Toxicity (11,300) Subchronic/Chronic Toxicity (15,100) Acute Toxicity (68,500) O O N All assessment captured in CHS External Data Sources: BIBRA*, Cal Prop 65*, CTFA*, HERA*, HPV*, OECD*, IPCS*, NICNAS*, RIFM/FEMA*, SCCP*, WHO/JECFA*, SciFinder, ToxNet, ATSDR, CPDB, ECETOC, ECB, IARC, Thompson/MicroMedix, NTP, RTECS/NIOSH, Scopus, TSCATS, others

6 Flow chart of new analog identification & evaluation framework Search strategy 1. Structural features 2. Key functional groups The target chemical Chemical & Toxicological Databases Revised strategy Initial search results 1.DiscoveryGate TM, Metabolism TM 2. Literature reports 3. Substructure search results 4. Expert judgment 5. Meteor software TM Candidate analogs with relevant toxicity data Metabolic routes &major metabolites of the target Ranking based upon Structural similarity Reactivity similarity Metabolism similarity Phys-Chem properties 1. DEREK TM 2. Ashby structural alerts 3. Principles of Chem, Biochem-toxicology 4. Expert judgment 5. ACD/Labs TM Submission for tox data & uncertainty ranking review to fill the data gap Final analog package includes: 1. Categorized analogs w/short rational explanation & their phys-chem properties 2. Major metabolic pathways & major metabolites of the target

7 Decision tree for categorizing analogs Candidate Analogs with relevant toxicity data 1 Do the target & analog have similar structural features & chemical reactivity? 5 9 Could the target & analog Do the target & analog metabolize to each share a other or converge to a major substructural common stable metabolite or feature or key functional reactive metabolite with the group? same mode of action? 2 Do the target & analog contain different or potentially different alert functional groups? 3 Do the target & analog have similar metabolic pathways? 6 Could the different alert groups potentially change the toxicity of the analog relative to the target? 7 Could metabolism result in different bioactivation pathways? 10 t suitable Could any other part of themoleculehavethe potential to change the toxicity of the analog relative to the target? 4 Do the target & analog have similar phys-chem properties? 8 Could these phys-chem differences fundmently alter toxicological profile? Suitable Suitable with interpretation Suitable with preconditions

8 Searching GRASP- Substructure Searching O O O N O N O Search Structure O O F N HO HO N

9 Output Substructure Searching

10 Expert system decision tree for repro/dev toxicity 10 I II Chemicals Organic compds Contains a cyclic ring IV Belongs to multifunctional group subst. (at the terminal carbon) < C8 hydrocarbons (substituents: halogens, NH 2, SH, OH, OR, CN) Belongs to: multihalogenated (Cl, Br) < C4 alkanes or alkenes Metallic derivatives Belong to Al, As, B, Cd, Cu, Cr, Zn, Mn, acids, oxides chlorides or Pb, Hg chlorides & Me, Et derivatives Belongs to: 1) small or III branched alkyl benzene and Core structure PAHs; 2) poly-subst.(cl, Br, contains aromatic or NO 2 ) benzene, oxdibenzene; heteroaromatic ring bi-benzene; 3) BMHCA-like; 4) alpha aryloxy subst. 2 aliphatic acid derivatives Belongs to 1) chloro subst. (6 Cls) cyclohexane; 2) cyclophosphamide, cycloheximide; 3) arabinopyranose; 4) isotretinoin & retinoids; 5) pyrimidine & purine derivatives V Belongs to < C8 carboxylic acids, their precursors (alcohols, aldehydes, esters) or amides, ureas & carbamates Miscellaneous chemicals: Belongs to alkylation agents, bis(2-ethylhexyl) hexanedioic ester, methyl carbamodithioic acid etc. VI Belongs to saturated, < C8 carboxylic acids or their precursors (alcohols, aldehydes, esters) 8 Precedented reproductive & developmental toxic potential 3 7 Belongs to: 1) vinyl amides, aldehydes & esters; 2) C1- C4 amides and N-alkyl subst. amides, ureas, thioureas, carbamates ER binding chemicals: steroids, flavones alkylphenols, DES-like deriv. biphenyls with OH, DDT-like, salicylates, parabens, phthalates, tamoxifen-like, alkoxy phenols, diphenyl alkanes AR binding chemicals: N-aryl subst. urea, carbamides Precedented reproductive & developmental toxic potential 5 Belongs to: 1) alpha halogens (Cl, Br); alphaalkoxyl (-OR, R is < C5 alkyl chain); alpha-alkyl (C2 to C3) substituted carboxylic acids or their precursors; 2) adipate derivates; 3) C1-C4 non-branched alcohols 4 1) Sulfamoyl, sulfonic acid, subst. benzoic acid, toluenesulfonamide; 2) benzidineazo and ethyl aminoazo compds; 3) Triazoles & pyridyl triazenes; 4) 2,4-diamino pyrimidine Miscellaneous Drugs:diphenyl hydantoin, thalidoamide, benzhydryl piperazine, leucoalkyl violet, nitrofural, chloromazine, codine, morphine, xanthotoxin; Antibiotics: actinomycin D, mitomycin C, puromycin, streptomycin, lincomycin, gentiah violet, oxytetracycline; Natural chemicals: berberine, emodin, hinokitiol Unprecedented repro/dev toxic potential 11 Belongs to di-functional group (NH 2,SH,OH, OR, CN) subst. C2 to C6 hydrocarbon or repeating C2 units Unprecedented repro/dev toxic potential

11 Key criteria for ER binding 1 Hydrogen bonding ability of the phenolic ring. 4 Ring structure 2 Hydrogen bond donor and O-O distance. 3 Precise steric hydrophobic centers. Satisfactory hydrophobicity (log p).

12 Expert system decision tree for ER binding (EPA)

13 Current research in supporting biological activity assessments Toxicogenomics Predictive toxicology Evaluation of global gene changes in tissues of interest in vitro, with comparisons to known toxicants Ishikawa cells Rat hepatocytes Human hepatocytes connectivity mapping Mechanistic understanding Improved dose-response assessment

14 From Liu et al., 2005

15 Fluid imbibition Cell Proliferation Epithelium remodeling Regression to basal level Time (h) Transcription factors, cell signaling, vascular permeability, growth factors mrna and protein synthesis Cell growth, differentiation, suppression apoptosis, Cell cycle regulators DNA replication and cell division Tissue remodeling and cytoarchitecture Immune response Adapted from Fertuck et al. (2003); Moggs et al. (2004); and Naciff et al. (2007)

16 KEGG Cell cycle Example EE BPA EE-RAT-24hr (Up-regulated) Gen

17 Connectivity Mapping Establish connections between biological states using gene expression data A simpler approach to analyzing toxicogenomics data to identify common mechanisms Data are from simple cell types, but these contain a diverse range of small molecule receptors Most highly up- and down-regulated genes are considered High degree of concordance across mechanisms

18 Lovastatin as an example From Smalley, Gant and Zhang, 2010

19 Dynamic Modeling of Local Effects Linking: Pharmacokinetic model of dermal absorption Quantitative metabolism predictions Kinetics of peptide reactivity Formation of p-hydroxybenzoic acid (nmol/min/mg) Vmax = 8.8 Km = Concentration of Butyl Paraben (µm)

20 Acknowledgements Cheminformatics Karen Blackburn Shengde Wu AIM and ACES Teams Dynamic Models Joanna Jaworska John Troutman Toxicogenomics Jorge Naciff Gary Overmann Greg Carr Yuching Shan Xiahong Wang Nadira deabrew Blad Ovando