Predicting Successes and Failures of Clinical Trials

Transcription

1 Predicting Successes and Failures f Clinical Trials Alex Ivliev, PhD Principal Scientist May 2018

2 2 Intrductin Despite imprvements acrss all stages f drug develpment, the rate f drug attritin due t clinical trial failures has risen substantially. A substantial prtin f these failures ccur fr safety reasn. A key area f imprvement has been the screening fr drugs likely t fail clinical trials.

3 3 Drug-likeness measures Widely accepted as a useful guide fr filtering txic mlecules in the early stages f drug discvery Lipinski prpsed this cncept in 1997 with the Rule f 5 (R5) Set f 4 physicchemical features (i) < 5 hydrgen-bnd dnrs; (ii) mlecular mass <500; (iii) calculated lg P<5 (partitin cefficient); (iv) < 10 f hydrgen-bnd acceptrs. Helps determine if a chemical cmpund with a certain pharmaclgical r bilgical activity has prperties that wuld make it a likely rally active drug in humans. Candidate drugs that cnfrm t the R5 tend t have lwer attritin rates during clinical trials and hence have an increased chance f reaching the market.

4 4 Drug-likeness measures R5 is a cnservative measure, and passing the rule des nt guarantee drug-likeness. Mdified rules have been prpsed since: Veber s rule (2002) Ghse s rule (1999) Quantitive Estimate f Drug-Likeness (QED) (2012) These include additinal drug prperties, e.g. plar surface area t predict biavailability. These cncepts have been shwn t reduce attritin rates, hwever verall clinical trial attritin rates cntinue t increase. Mst drugs that failed due t txicity (FTT) pass these rules. S we are currently lacking in an apprach t distinguish between FDA apprved drugs and thse FTT.

5 5 Gayvert et al Publicatin This paper intrduced a data-driven apprach (PrOCTOR) that directly predicts the likelihd f txicity in clinical trials. Apprach uses a cmbinatin f drug structure, drug likeness, target expressin and ther features.

6 6 PrOCTOR mdel 48 features: 10 mlecular prperties, 24 target based features, 4 druglikeness rule features

7 7 Target based features Drug targets anntated frm DrugBank Tissue expressin data calculated frm Gentype-Tissue Expressin (GTEx) prject Lss f functin mutatin frequency in target gene extracted frm Exme Aggregatin Cnsrtium (ExAC) database Netwrk cnnectivity (degree and betweenness) f target was cmputed using aggregated gene-gene interactin netwrk Regulatry netwrk was frm ENCODE data, Metablic enzyme netwrk based n cmpund reactins in KEGG. Phsphrylatin netwrk, signaling netwrk cnstructed frm the SignalLink database. Reactme and NetPath als used.

8 8 Mdeling 784 FDA apprved drugs and 100 FTT drugs Randm Frest mdels built ver 50 btstrapped decisin trees using the 48 features. A sub-sampling apprach is used t accunt fr the imbalanced rati f apprved drugs t FTT drugs Randmly sampling the FDA-apprved class f samples t the size f the FTT drugs. T reduce the dds f pr representatives being sampled, this was repeated 30 times. The labels were assigned by taking the cnsensus acrss the set f btstrapped trees. This apprach als yields a prbability fr each test sample. This prbability is used t calculate an dds scre = P(apprval)/P(failure)

9 9 Randm Frest Ensemble apprach: A grup f weak learners that tgether frm a strng learner Begin as a decisin tree (weak learner). RF cmbines trees (strng learner). Samples N cases at randm with replacement Randmly select m predictr variables Split n the nde that prvides best perfrmance Repeat

10 10 Perfrmance ROC curve fr PrOCTOR and ther scres. AUC = Sens = Spec = Statistically significantly better perfrmance than the ther metrics.

11 Feature imprtance in predicting FTT 11

12 Feature imprtance in predicting FTT 12

13 13 Validatin Mdel applied t Eurpean and Japanese apprved drugs.

14 14 Validatin: Side effects Adverse events ccur mre frequently in predicted failed txic clinical trial (FTT) drugs cmpared with predicted apprved drugs.

15 15 Summary A new apprach that includes features beynd the widely accepted mlecular prperties f a cmpund was highly effective. Data-driven apprach (PrOCTOR) that incrprates the target-based infrmatin related t a drug, alng with the established chemical prperties t predict txic in clinical trial utcmes. PrOCTOR was able t significantly separate drugs that were txic in clinical trials frm FDAapprved drugs. PrOCTOR identified individual features, as well as cmbinatins f features, that predict txicity (r absence f txicity) and thus may help guide the ratinal drug develpment prcess t design less txic mlecules. Furthermre PrOCTOR may als help flag drugs fr increased pst-apprval surveillance f adverse effects and txicity.

16 16 Clarivate Implementatin Clarivate Gayvert et al AUC = 0.83 AUC = 0.82