Full Speed into an Alternative Future

1. Full Speed into an Alternative Future Bob Chapin Pfizer DART Group Groton, CT

2. Background and Stage-Setting • I spent 18 yrs at NIEHS doing male reproductive tox, developing methods in primary cell culture, running a lab and overseeing contract studies. • Been at Pfizer for 9 yrs now, in a lab which develops in vitro predictive assays and troubleshoots DART issues. • I am a very strong advocate of and believer in a future for toxicology based increasingly on measuring biochemical and genomic responses in cells in culture, and in computational modeling of complex systems. • My comments will come mostly from the pharma perspective, we’ll spend less time on environmental chemicals.

3. What are the motivations to get safety right? • Chasing positives: • The more specific and beneficial our meds can be, the more people will be helped by them. • Safer meds have an easier time finding a new indication. • Safer and more specific pesticides (“plant protection agents”) will provide more food for a hungry world with less environmental damage • Avoiding negatives • Every adverse effect is undesired • No one wants them; toll of human suffering, etc

4. Safety Assessment • Current state: We give animals more and more of a compound and watch what goes wrong. We then examine the low end of the dose-response curve and extrapolate that to humans based on experience and some “safety factors”. It’s certainly not perfect, but it’s worked pretty well (we think). • “Alternative models” are cell culture or tissue culture methods (or any other method) which reduce the numbers of animals treated with toxicants.

5. Alternatives going mainstream: • Future state: as laid out in the NAS book “Toxicity Testing in the 21st Century”. Focused on cell culture (with human cells) and progressively more computational predictions. • We’ll use the term “predictive model”. This can be • a well-characterized cell culture system whose data are massaged a certain way to give a prediction of toxicity • A combination of different kinds of data from cell cultures which, when combined using a certain statistical method, yields an estimate of the in vivo activity of this compound • A combination of different computer programs (each of which predicts different things) which together predict the in vivo toxicity of an exposure

6. Historically Safety assessors have relied on animals because • Assumptions of relevance based on conserved evolutionary features • Have integrated ADME (absorption, distribution, metabolism, and excretion) • Have integrated physiology that allows for recovery and adaptation • All target tissues are there in the relevant milieus • The target tissues have all the appropriate cells there in the right configuration

7. Animal-based systems • May be all right, but the assumed predictivity may not be all it’s cracked up to be. • Harry Olson et al* did a survey and reported that there was ≈ 43% predictivity of human response from rats alone. Using all animal models, the predictivity was ≈ 70%. This is depressingly low. • Animals “get the basic physiology right” (control of heart rate and blood pressure, steroid hormones, etc), but many toxic responses differ between rodents and humans, which tells me that there are subtle differences in biology that are critically important, and we don’t yet know what they are. *H Olson et al. Concordance of the toxicity of pharmaceuticals in humans and in animals. Regulatory Toxicology and Pharmacology 32: 56-67, 2000.

8. And to build a tool (like a model for predicting toxicity), we need to know what the critical parts are and how they fit together. That is: • If we commit to trying to model toxic responses using in vitro and in silico tools, we will need to know all the important component parts and how they interact and feed-back and -forward. • Absorption, distribution (protein binding, delivery to peripheral tissues), metabolism (which of the P450’s is used preferentially, whether a toxic metabolite will be formed, whether this will interfere with basal body functions, etc) and excretion (will it harm the kidneys? Will it deplete the liver of critical glutathione?, etc, etc) • What are the off-target targets? (the 800 lb gorilla) • What will Cmpd X do to the foci that control heart rate, blood pressure, breathing control, adrenal function, pacreatic function, etc, etc. • As it turns out, this is exceptionally difficult:

9. Current state (a very small sample of very focused efforts): True Positives (Toxic) • Pfizer’s DART models seem to have a glass ceiling at about 70% predictivity (n > 80 compounds). • ECVAM (DART): ≈ 80% for the first 20 compounds, then 2/13 for a second test set (15% correct). • A consortium of several pharma is using Zebrafish as a model for developmntal tox, and is struggling to get much past 50% predictive. • A general safety prediction group is struggling with the false negative predictions for truly-toxic compounds True Negatives Predicted Negative 82 88 ~8% False Positive Predicted Toxic 10 113 “clean” “findings”

10. WTF?? • We probably don’t know the right things to measure (but at least one of those previous examples has been encyclopedic in their examination of endpoints) • For those groups reporting success, they often are not testing enough chemicals: • Or it could be that the cultures don’t capture enough (or the right) biological complexity Chapin’s Curve of Modeling Despair 90 % Predictivity 10 300 100 Number of compounds

11. Paracrine: the relationship when 1 cells secretes something that affects its neighbor. Schwartz and Holst

12. Another example of paracrine complexity Ten Broek et al., J Cell Physiol. 224:7-16, 2010

13. Paracrine effects • So are these key, or just some biological jaw-droppers that we can marvel at and then ignore? • So far we’ve been following the Einstein mantra (“Make everything as simple as possible, but no simpler”), and it’s often worked well enough, but certainly not always. • Perhaps the targeted addition of complexity to our cultures will make the cells feel more at home, and they’ll give better answers. • We’ll also need to better intuit what the key responses are (i.e., as scientists, we probably need to think differently).

14. Industry will embrace these when their predictivity become dependable (“All models are wrong, but some are useful.”) or  when they are required as part of a regulatory submission package. • Currently, predictive models (and their component assays) are used for internal prioritization, but because regulatory decision-makers have relatively little experience with these models, in vitro data add little to regulatory safety packages.

15. Role of stem cells • Stem cells can (theoretically, and increasingly in real life) turn into any cell type. • They can also be used to model (or recapitulate) the process of differentiation that an embryo normally undergoes. • Multiple cell types are (probably) important in a toxicity response because of paracrinology. So any model that gives rise to multiple related cell types (normally found together in vivo) could be more useful than a plate full of a single cell type (if we knew what to measure).