Large-scale prediction and testing of drug activity on side-effect targets.

Discovering the unintended 'off-targets' that predict adverse drug reactions is daunting by empirical methods alone. Drugs can act on several protein targets, some of which can be unrelated by conventional molecular metrics, and hundreds of proteins have been implicated in side effects. Here we use a computational strategy to predict the activity of 656 marketed drugs on 73 unintended 'side-effect' targets. Approximately half of the predictions were confirmed, either from proprietary databases unknown to the method or by new experimental assays. Affinities for these new off-targets ranged from 1 nM to 30 µM. To explore relevance, we developed an association metric to prioritize those new off-targets that explained side effects better than any known target of a given drug, creating a drug-target-adverse drug reaction network. Among these new associations was the prediction that the abdominal pain side effect of the synthetic oestrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme cyclooxygenase-1. The clinical relevance of this inhibition was borne out in whole human blood platelet aggregation assays. This approach may have wide application to de-risking toxicological liabilities in drug discovery.

Decentralized clinical trial design using blood microsampling technology for serum bioanalysis.

Background: Alternatives to phlebotomy in clinical trials increase options for patients and clinicians by simplifying and increasing accessibility to clinical trials. The authors investigated the technical and logistical considerations of one technology compared with phlebotomy. Methodology: Paired samples were collected from 16 donors via a second-generation serum gel microsampling device and conventional phlebotomy. Microsamples were subject to alternative sample handling conditions and were evaluated for quality, clinical testing and proteome profiling. Results: Timely centrifugation of blood serum microsamples largely preserved analyte stability. Conclusion: Centrifugation timing of serum microsamples impacts the quality of specific clinical chemistry and protein biomarkers. Microsampling devices with remote centrifugation and refrigerated shipping can decrease patient burden, expand clinical trial populations and aid clinical decisions.

Leveraging patient-centric sampling for clinical drug development and decentralized clinical trials: Promise to reality.

Advances in the technologies to enable patient-centric sampling (PCS) have the potential to improve blood sample collection by enabling clinical trial participants to collect samples via self-collection or with the help of a caregiver in their home. Typically, blood samples to assess pharmacokinetics and pharmacodynamics of a drug during clinical development are collected at a clinical site via venous blood draw. In this position paper by the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ), the potential value PCS can bring to patients, to the clinical datasets generated, and to clinical trial sponsors is discussed, along with considerations for program decision making, bioanalytical feasibility, operations, and regulatory implications. With an understanding of the value of PCS and considerations when implementing during clinical drug development, we can bring the promise of PCS closer to reality and enable decentralized clinical trials.

Evaluation of a novel blood microsampling device for clinical trial sample collection and protein biomarker analysis.

Aim: Evaluation of a novel microsampling device for its use in clinical sample collection and biomarker analysis. Methodology: Matching samples were collected from 16 healthy donors (ten females, six males; age 42 ± 20) via K2EDTA touch activated phlebotomy (TAP) device and phlebotomy. The protein profile differences between sampling groups was evaluated using aptamer-based proteomic assay SomaScan and selected ELISA. Conclusion: Somascan signal concordance between phlebotomy- and TAP-generated samples was studied and comparability of protein abundances between these blood sample collection methods was demonstrated. Statistically significant correlation in selected ELISA assays also confirmed the TAP device applicability to the quantitative analysis of protein biomarkers in clinical trials.

Plate-based diversity selection based on empirical HTS data to enhance the number of hits and their chemical diversity.

Typically, screening collections of pharmaceutical companies contain more than a million compounds today. However, for certain high-throughput screening (HTS) campaigns, constraints posed by the assay throughput and/or the reagent costs make it impractical to screen the entire deck. Therefore, it is desirable to effectively screen subsets of the collection based on a hypothesis or a diversity selection. How to select compound subsets is a subject of ongoing debate. The authors present an approach based on extended connectivity fingerprints to carry out diversity selection on a per plate basis (instead of a per compound basis). HTS data from 35 Novartis screens spanning 5 target classes were investigated to assess the performance of this approach. The analysis shows that selecting a fingerprint-diverse subset of 250K compounds, representing 20% of the screening deck, would have achieved significantly higher hit rates for 86% of the screens. This measure also outperforms the Murcko scaffold-based plate selection described previously, where only 49% of the screens showed similar improvements. Strikingly, the 2-fold improvement in average hit rates observed for 3 of 5 target classes in the data set indicates a target bias of the plate (and thus compound) selection method. Even though the diverse subset selection lacks any target hypothesis, its application shows significantly better results for some targets-namely, G-protein-coupled receptors, proteases, and protein-protein interactions-but not for kinase and pathway screens. The synthetic origin of the compounds in the diverse subset appears to influence the screening hit rates. Natural products were the most diverse compound class, with significantly higher hit rates compared to the compounds from the traditional synthetic and combinatorial libraries. These results offer empirical guidelines for plate-based diversity selection to enhance hit rates, based on target class and the library type being screened.

Which aspects of HTS are empirically correlated with downstream success?

High-throughput screening (HTS) is a well-established hit-finding approach used in the pharmaceutical industry. In this article, recent experience at Novartis with respect to factors influencing the success of HTS campaigns is discussed. An inherent measure of HTS quality could be defined by the assay Z and Z' factors, the number of hits and their biological potencies; however, such measures of quality do not always correlate with the advancement of hits to the later stages of drug discovery. Also, for many target classes, such as kinases, it is easy to identify hits, but, as a result of selectivity, intellectual property and other issues, the projects do not result in lead declarations. In this article, HTS success is defined as the fraction of HTS campaigns that advance into the later stages of drug discovery, and the major influencing factors are examined. Interestingly, screening compounds in individual wells or in mixtures did not have a major impact on the HTS success and, equally interesting, there was no difference in the progression rates of biochemical and cell-based assays. Particular target types, assay technologies, structure-activity relationships and powder availability had a much greater impact on success as defined above. In addition, significant mutual dependencies can be observed - while one assay format works well with one target type, this situation might be completely reversed for a combination of the same readout technology with a different target type. The results and opinions presented here should be regarded as groundwork, and a plethora of factors that influence the fate of a project, such as biophysical measurements, chemical attractiveness of the hits, strategic reasons and safety pharmacology, are not covered here. Nonetheless, it is hoped that this information will be used industry-wide to improve success rates in terms of hits progressing into exploratory chemistry and beyond. The support that can be obtained from new in silico approaches to phase transitions are also described, along with the gaps they are designed to fill.

Chemogenomic data analysis: prediction of small-molecule targets and the advent of biological fingerprint.

Chemogenomics comprises a systematic relationship between targets and ligands that are used as target modulators in living systems such as cells or organisms. In recent years, data on small molecule-bioactivity relationships have become increasingly available, and consequently so have the number of approaches used to translate bioactivity data into knowledge. This review will focus on two aspects of chemogenomics. Firstly, in cases such as cell-based screens, the question of which target(s) a compound is modulating in order to cause the observed phenotype is crucial. In silico target prediction tools can suggest likely biological targets of small molecules via data mining in target-annotated chemical databases. This review presents some of the current tools available for this task and shows some sample applications relevant to a pharmaceutical industry setting. These applications are the prediction of false-positives in cell-based reporter gene assays, the prediction of targets by linking bioassay data with protein domain annotations, and the direct prediction of adverse reactions. Secondly, in recent years a shift from structure-derived chemical descriptors to biological descriptors has occurred. Here, the effect of a compound on a number of biological endpoints is used to make predictions about other properties, such as putative targets, associated adverse reactions, and pathways modulated by the compound. This review further summarizes these "performance" descriptors and their applications, focusing on gene expression profiles and high-content screening data. The advent of such biological fingerprints suggests that the field of drug discovery is currently at a crossroads, where single target bioassay results are supplanted by multidimensional biological fingerprints that reflect a new awareness of biological networks and polypharmacology.

Angular variances for internal bond rotations of side chains in GXG-based tripeptides derived from (13)C-NMR relaxation measurements: Implications to protein folding.

The study of backbone and side-chain internal motions in proteins and peptides is crucial to having a better understanding of protein/peptide "structure" and to characterizing unfolded and partially folded states of proteins and peptides. To achieve this, however, requires establishing a baseline for internal motions and motional restrictions for all residues in the fully, solvent-exposed "unfolded state." GXG-based tripeptides are the simpliest peptides where residue X is fully solvent exposed in the context of an actual peptide. In this study, a series of GXG-based tripeptides has been synthesized with X being varied to include all twenty common amino acid residues. Proton-coupled and -decoupled (13)C-nmr relaxation measurements have been performed on these twenty tripeptides and various motional models (Lipari-Szabo model free approach, rotational anisotropic diffusion, rotational fluctuations within a potential well, rotational jump model) have been used to analyze relaxation data for derivation of angular variances and motional correlation times for backbone and side-chain chi(1) and chi(2) bonds and methyl group rotations. At 298 K, backbone motional correlation times range from about 50 to 85 ps, whereas side-chain motional correlation times show a much broader spread from about 18 to 80 ps. Angular variances for backbone phi,psi bond rotations range from 11 degrees to 23 degrees and those for side chains vary from 5 degrees to 24 degrees for chi(1) bond rotations and from 5 degrees to 27 degrees for chi(2) bond rotations. Even in these peptide models of the "unfolded state," side-chain angular variances can be as restricted as those for backbone and beta-branched (valine, threonine, and isoleucine) and aromatic side chains display the most restricted motions probably due to steric hinderence with backbone atoms. Comparison with motional data on residues in partially folded, beta-sheet-forming peptides indicates that side-chain motions of at least hydrophobic residues are less restricted in the partially folded state, suggesting that an increase in side-chain conformational entropy may help drive early-stage protein folding. Copyright 1999 John Wiley & Sons, Inc.

Electronic processing of informed consents in a global pharmaceutical company environment.

We present an electronic capture tool to process informed consents, which are mandatory recorded when running a clinical trial. This tool aims at the extraction of information expressing the duration of the consent given by the patient to authorize the exploitation of biomarker-related information collected during clinical trials. The system integrates a language detection module (LDM) to route a document into the appropriate information extraction module (IEM). The IEM is based on language-specific sets of linguistic rules for the identification of relevant textual facts. The achieved accuracy of both the LDM and IEM is 99%. The architecture of the system is described in detail.

Cardiac ion channel safety profiling on the IonWorks Quattro automated patch clamp system.

The normal electrophysiologic behavior of the heart is determined by the integrated activity of specific cardiac ionic currents. Mutations in genes encoding the molecular components of individual cardiac ion currents have been shown to result in multiple cardiac arrhythmia syndromes. Presently, 12 genes associated with inherited long QT syndrome (LQTS) have been identified, and the most common mutations are in the hKCNQ1 (LQT1, Jervell and Lange-Nielson syndrome), hKCNH2 (LQT2), and hSCN5A (LQT3, Brugada syndrome) genes. Several drugs have been withdrawn from the market or received black box labeling due to clinical cases of QT interval prolongation, ventricular arrhythmias, and sudden death. Other drugs have been denied regulatory approval owing to their potential for QT interval prolongation. Further, off-target activity of drugs on cardiac ion channels has been shown to be associated with increased mortality in patients with underlying cardiovascular diseases. Since clinical arrhythmia risk is a major cause for compound termination, preclinical profiling for off-target cardiac ion channel interactions early in the drug discovery process has become common practice in the pharmaceutical industry. In the present study, we report assay development for three cardiac ion channels (hKCNQ1/minK, hCa(v)1.2, and hNa(v)1.5) on the IonWorks Quattro™ system. We demonstrate that these assays can be used as reliable pharmacological profiling tools for cardiac ion channel inhibition to assess compounds for cardiac liability during drug discovery.

A lead discovery strategy driven by a comprehensive analysis of proteases in the peptide substrate space.

We present here a comprehensive analysis of proteases in the peptide substrate space and demonstrate its applicability for lead discovery. Aligned octapeptide substrates of 498 proteases taken from the MEROPS peptidase database were used for the in silico analysis. A multiple-category naïve Bayes model, trained on the two-dimensional chemical features of the substrates, was able to classify the substrates of 365 (73%) proteases and elucidate statistically significant chemical features for each of their specific substrate positions. The positional awareness of the method allows us to identify the most similar substrate positions between proteases. Our analysis reveals that proteases from different families, based on the traditional classification (aspartic, cysteine, serine, and metallo), could have substrates that differ at the cleavage site (P1-P1') but are similar away from it. Caspase-3 (cysteine protease) and granzyme B (serine protease) are previously known examples of cross-family neighbors identified by this method. To assess whether peptide substrate similarity between unrelated proteases could reliably translate into the discovery of low molecular weight synthetic inhibitors, a lead discovery strategy was tested on two other cross-family neighbors--namely cathepsin L2 and matrix metallo proteinase 9, and calpain 1 and pepsin A. For both these pairs, a naïve Bayes classifier model trained on inhibitors of one protease could successfully enrich those of its neighbor from a different family and vice versa, indicating that this approach could be prospectively applied to lead discovery for a novel protease target with no known synthetic inhibitors.

Use of ligand based models for protein domains to predict novel molecular targets and applications to triage affinity chromatography data.

The elucidation of drug targets is important both to optimize desired compound action and to understand drug side-effects. In this study, we created statistical models which link chemical substructures of ligands to protein domains in a probabilistic manner and employ the model to triage the results of affinity chromatography experiments. By annotating targets with their InterPro domains, general rules of ligand-protein domain associations were derived and successfully employed to predict protein targets outside the scope of the training set. This methodology was then tested on a proteomics affinity chromatography data set containing 699 compounds. The domain prediction model correctly detected 31.6% of the experimental targets at a specificity of 46.8%. This is striking since 86% of the predicted targets are not part of them (but share InterPro domains with them), and thus could not have been predicted by conventional target prediction approaches. Target predictions improve drastically when significance (FDR) scores for target pulldowns are employed, emphasizing their importance for eliminating artifacts. Filament proteins (such as actin and tubulin) are detected to be 'frequent hitters' in proteomics experiments and their presence in pulldowns is not supported by the target predictions. On the other hand, membrane-bound receptors such as serotonin and dopamine receptors are noticeably absent in the affinity chromatography sets, although their presence would be expected from the predicted targets of compounds. While this can partly be explained by the experimental setup, we suggest the computational methods employed here as a complementary step of identifying protein targets of small molecules. Affinity chromatography results for gefitinib are discussed in detail and while two out of the three kinases with the highest affinity to gefitinib in biochemical assays are detected by affinity chromatography, also the possible involvement of NSF as a target for modulating cancer progressions via beta-arrestin can be proposed by this method.

Mapping adverse drug reactions in chemical space.

We present a novel method to better investigate adverse drug reactions in chemical space. By integrating data sources about adverse drug reactions of drugs with an established cheminformatics modeling method, we generate a data set that is then visualized with a systems biology tool. Thereby new insights into undesired drug effects are gained. In this work, we present a global analysis linking chemical features to adverse drug reactions.

Gaining insight into off-target mediated effects of drug candidates with a comprehensive systems chemical biology analysis.

We present a workflow that leverages data from chemogenomics based target predictions with Systems Biology databases to better understand off-target related toxicities. By analyzing a set of compounds that share a common toxic phenotype and by comparing the pathways they affect with pathways modulated by nontoxic compounds we are able to establish links between pathways and particular adverse effects. We further link these predictive results with literature data in order to explain why a certain pathway is predicted. Specifically, relevant pathways are elucidated for the side effects rhabdomyolysis and hypotension. Prospectively, our approach is valuable not only to better understand toxicities of novel compounds early on but also for drug repurposing exercises to find novel uses for known drugs.