16 th Online World Conference on Soft Computing in Industrial Applications Special Session

1. 16thOnline World Conference on Soft Computing in Industrial Applications Special Session Soft Computing Methods in Pharmaceutical and Medical Sciences In silicobinaryclassification model for hERGliability screening Barbara Wiśniowska, Aleksander Mendyk Unit of Pharmacoepidemiology and Pharmacoeconomics Jagiellonian University Medical College

2. drugwithdrawals Over the last two decades, a number of blockbuster drugs have been withdrawn and others have received new “black box” warnings due to adverse cardiac effects. In addition, lead compounds or drug candidates are frequently terminated at late stages of drug development due to cardiac safety concerns. Both of these factors can significantly impact the overall cost of drug discovery; consequently, there is an increasing interest in early assessing the cardiac liability of drugs which are currently under development. What is more cardiotoxicity assessment is a compulsory element of the drug development process required by drug agencies. 16th Online World Conference on Soft Computing in Industrial Applications

3. INa ICa ICa Ito membrane potential Iks (KCNQ1 channel) IKr (hERG channel) time TdP – Torsades de Pointes Proarrhythmic risk, defined as the TdP occurrence possibility, can be induced by the perturbation of the ionic balance of the heart. The low incidence of TdP and possibly lethal effects entail the necessity of the surrogate markers usage for the risk estimation. It is well accepted that delayed ventricular repolarization (QT prolongation on the ECG graph)is a surrogate biomarker linked with enhanced proarrhythmic risk. The main cause underlying the QT prolongation is a blockade of the hERG potassium channels which are responsible for the delayed rectifier potassium current (Ikr) in cardiomyocytes. Thus hERGchannel blocking potency, namely the drug concentration that produce half-maximal channel inhibition (IC50), is accepted, recommended and widely used as one of the cardiac liability marker used for the TdP risk screening. TdP LQTS • mechanism • inhibition of the rapid delayed rectifier potassium current Ikr (channel encoded by the hERGgene) • channel encoded by the hERG gene 16th Online World Conference on Soft Computing in Industrial Applications

4. underpressure micropipette proarrhythmicliabilityassessment connection 10 - 100 GΩ connection 50 MΩ in vitro cell membrane ThehERG blocking potency, expressed by IC50 – half maximal inhibitory concentration of tested compund - ismost widely used predictor of cardiac liability definedat the earliest stages of drug development is. There are several in vitro approaches used to quantify drug-hERG interactions including rubidium-flux and radioligand binding assays, in vitro electrophysiology measurements and fluorescence-based assays. Among these methods, electrophysiological assays, especially the manual PatchClamp technique in the whole cell mode,are considered as a ‘gold standard’. Patch Clamp method is based on the application of voltage stimuli to cells and measurement of the direct current from ion channels within the membrane patch. The amplitude of tail current is measuredin absence and presence of different concentrationsof the tested compound in bath solution. Once the data are collected, the concentration producing half-maximal block of the hERG potassium current (IC50) can be calculated by fitting the data with Hill equation and obtaining a concentration-response curve. This technique is labor-intensive and low-throughput but it provides accurate in-depth information on the effect of drugs on ion channels. • rubidium flux • radioactive ligands binding • fluorescence assessment • electrophysiological methods (HEK, CHO, XO cell lines) - patch clamp Inhibition (%) log concentration (µM) 16th Online World Conference on Soft Computing in Industrial Applications

5. proarrhythmicliability in silico data base The first step of the work included bibliographic analysis of the available literature sources and the collection of quantitative data describing the hERG channel blocking phenomenon. As a result, the data set of experimentally measured IC50 values (the concentration which produces half-maximal block of the hERG channel) with the relevant information regarding factors influencing the laboratory measurement results (electrophysiological settings) was created. Every single paper and data record was carefully checked to meet the inclusion criteria to assure the high quality of the data. > 300 The final data set relates to over 300 publically available papers. 447 The final data set consisted of 447 IC50 values (half-maximal inhibitory concentration) for 175 different compounds accompanied by a description of the in vitro experimental settings. The databaseisfreely available after registration on the Tox-Comp project website www.tox-portal.net 175 3 In all experiments three models were used (HEK, CHO, XO). hold – holding potential depol – depolarizationlevel measure – measurementvoltage transf – transfectiontype K+ - potassiumionsconcentration in bathsolution t1 – duration of depolarizationpulse 16th Online World Conference on Soft Computing in Industrial Applications

6. proarrhythmicliability in silico extrapolationfactors Temperature extrapolation factor HEK physiological temperature/HEK room temperature Inter-system extrapolation factor Inter-system ratios As literature data analysis shows that the hERG interactions experiments carried out in different conditions with use of different in vitro systems for the same substance can result with different IC50 values, we proposed (Wiśniowska, Polak 2009, ToxicolMechMeth) extrapolation factors for inter-system (HEK, CHO, XO) and inter-temperature (room and physiological) IC50 values unification. The original IC50 values derived from in vitro experiments were scaled with the use of extrapolating factors. 16th Online World Conference on Soft Computing in Industrial Applications

7. proarrhythmicliability in silico methods & algorithms • OUTPUT:binary (unsafe/safe) - classification • INPUT: phys-chemdescriptors + in vitro experimentalsettings • ALGORITHMS: • ANN • Decision trees • BayesNet • VALIDATION: internal (10-fold CV) & external (dataset) The task, for the model to solve, was to classify compounds according to their cardiac safety. Therefore a flag, “safe” or “unsafe”, (encoded as 0 and 1, respectively), was assigned to each data record. The safety threshold was set at IC50 equal to 1 µM, on the basis of literature analysis. The selected cut-off value for hERG-blokers/non-blokers resulted in 250 records classified as “unsafe” and 197 as “safe”. The subsequent model development process included the selection of the input vector components and a classification algorithm. In addition to the parameters defining laboratory setting, over 100 descriptors of the physico-chemical properties were generated for each compound, using ChemAxon software (Marvin Beans). 16th Online World Conference on Soft Computing in Industrial Applications

8. proarrhythmicliability in silico methods & algorithms • INPUT: in vitro experimentalsettings+ phys-chemdescriptors • derived from the availableliterature • cellmodel - XO / CHO / HEK • temperature– room/phys • K+bathconcentration [mM] • t1 pulse [s] • t2 pulse [s] • holding potential [mV] • depolarizationlevel [mV] • measurementpotential [mV] 16th Online World Conference on Soft Computing in Industrial Applications

9. proarrhythmicliability in silico methods & algorithms • INPUT: in vitro experimentalsettings + phys-chemdescriptors • calculated in Marvin Beans package • sdf files either derived from PubChem or drawn in MarvinSketch • 41 plugins • 107 numeric inputs natively • 38parametersafter the sensitivityanalysis Sensitivity analysis was performed in order to reduce model complexity and to evaluate the impact of the reduction number of descriptors on the model’s generalization ability. 38 key variables were identified. The chosen descriptors include both those describing experimental settings, as well as the physico-chemical and geometric properties indicated in literature as crucial for drug-hERG channel binding 16th Online World Conference on Soft Computing in Industrial Applications

10. proarrhythmic potency assessment methods & algorithms • INPUT: in vitro experimentalsettings + phys-chemdescriptors • calculated in Marvin Beans package • Log P • Log D • … • rotatableboundcount • chiralcentercount • stereoisomercount • … • Largest_ring_size • Minimal_projection_radius/area • Maximal_projection_radius/area 16th Online World Conference on Soft Computing in Industrial Applications

11. proarrhythmicliability in silico methods & algorithms • OUTPUT:binary (unsafe/safe) - classification • INPUT: phys-chemdescriptors + in vitro experimentalsettings • ALGORITHMS: • ANN • Decision trees • BayesNet • VALIDATION: internal (10-fold CV) & external (dataset) In order to ensure the highest possible reliability of the final model three various validation modes were applied to the model performance assessment: standard 10-fold cross validation procedure (10-fold CV), enhanced 10-fold cross validation and a validation on an external test set of 55 records for both previously present (different in vitro models) and absent in native dataset structures. Modification of the standard 10-CV procedure was proposed. All information describing a particular drug was excluded from the test sets. It assured that the training and test sets were separated as each compound can belong exclusively to either training or test instances. Artificial neural networks and recursive partitioning methods were used for predictive model development. The specially written ANN simulator, the Nets2010, and WEKA software (Waikato Environment for Knowledge Analysis) were used to perform the model development. In this work classical multi-layer perceptrons, containing 1 to 6 hidden layers, were applied. All ANNs were trained by a back propagation algorithm with momentum and jog-of-weights modifications. The amount of training iteration varied from 10 000 to 5 000 000. The epoch size was set to 1. The training of ANNs involved random data record presentation with or without additional noise in the data (‘rand’ or ‘orig’ in the input data files description, respectively). The data was scaled linearly from 0.2 to 0.8 or from −0.8 to 0.8 (‘scale’ in the input data files description). Decision tress and random forests were developed with the use of the WEKA package. Different tree constructing algorithms with learning parameter modifications were tested. In addition to the single-algorithm-based models, modular systems (so called ‘expert committees’) combining several ANNs or an ANN with WEKA algorithms were tested. 16th Online World Conference on Soft Computing in Industrial Applications

12. proarrhythmicliability in silico results – 10 CV Around 800 classification models were developed and evaluated. The table presents the results for the enhanced 10-CV procedure of 5 best obtained models. The optimal model found during the numerical experimentation was based on the artificial neural network algorithm and composed of three hidden layers and a hyperbolic tangent activation function. The performance estimated in the enhanced 10-fold cross-validation procedure was 79% of the total correct classifications. All – overall classification rate; SE – sensitivity; SP – specificity; PPV – positive predictive value; NPV – negative predictive value 16th Online World Conference on Soft Computing in Industrial Applications

13. proarrhythmicliability in silico results – externalvalidation The best obtained architectures with identical settings kept, were then trained again with the use of the whole dataset as the learning set (447 records) and their performance was automatically verified, based on the external dataset. The overall classification rate in this procedure for the best algorithm was 87% All – overall classification rate; SE – sensitivity; SP – specificity; PPV – positive predictive value; NPV – negative predictive value 16th Online World Conference on Soft Computing in Industrial Applications

14. proarrhythmicliability in silico results – modular system Expert committees were prepared and tested with the purpose of further model quality improvement. The final classification model for hERG channel blockers was based on 2 and 3 neural networks with the highest prediction accuracy for hERG active and non-active compounds, respectively and the best WEKA algorithm. 16th Online World Conference on Soft Computing in Industrial Applications

15. proarrhythmicliability in silico results – modular system External validation set was composed of both new structures (subset “NEW”- 30 records) and molecules known to the system but assessed under different experimental conditions (subset “KNOWN” – 25 records). The analysis of incorrect decisions provided by the model shows that all of them concerned molecules absent in native dataset. This proves very good predictive value of the model as far as the experimental settings are concerned. Extrapolation is less pronounced in case of chemical structure of the compounds. It should be noted that six of the seven misclassifications are false-positives which means that safe compound was classified as hERG-blocker. This is probably better situation than false-negative predictions, because the risk of proceeding of the cardiotoxic compound and failure in later stages due to cardiac liability is low. - generalizationability Overall classification accuracy for the expertcommittee, estimated in 10-CV procedurewas 82% and 87% whentestedon the external test set.Thisprovethat combining different algorithms can have beneficial effects on prediction accuracy. However, no substantial improvement was observed. Nevertheless, the negative predictive value of the expert committee is considerably better than NPV of the best neural network. The high NPV (0.93) is beneficial, as the intended model function is the elimination of potentially unsafe compounds as early as possible. - generalizationability – in vitro settings & chemicalstructures All – overall classification rate; SE – sensitivity; SP – specificity; PPV – positive predictive value; NPV – negative predictive value 16th Online World Conference on Soft Computing in Industrial Applications

16. proarrhythmicliability in silico Tox-Comp platform The developedbinaryclassification model for hERGinhibitionisan element of the Tox-Comp.net platform. The Tox-Comp.net platform is flexible, modular system for the early assessment of the cardiotoxic potency of the chemical entities. It isfreely available after registration from the CompTox project website. http://www.tox-portal.net 16th Online World Conference on Soft Computing in Industrial Applications

17. acknowledgements team Sebastian Polak PhD Miłosz Polak Kamil Fijorek Anna Glinka Małgorzata Kozłowska projectfinanced by the Polish National Center for Research and Development LIDER project number LIDER/02/187/L-1/09 16th Online World Conference on Soft Computing in Industrial Applications

18. THANK YOU Unit of Pharmacoepidemiology and Pharmacoeconomics Jagiellonian University Medical College