Identification of NEK3 and MOK as novel targets for lithium

Lithium ion, commonly used as the carbonate salt in the treatment of bipolar disorders, has been identified as an inhibitor of several kinases, including Glycogen Synthase Kinase-3ß, for almost 20 years. However, both the exact mechanism of enzymatic inhibition and its apparent specificity for certain metalloenzymes are still a matter of debate. A data-driven hypothesis is presented that accounts for the specificity profile of kinase inhibition by lithium in terms of the presence of a unique protein environment in the magnesium-binding site. This hypothesis has been validated by the discovery of two novel potential targets for lithium, namely NEK3 and MOK, which are related to neuronal function.Ministerio de Economía y Competitivida

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Optimal HTS Fingerprint Definitions by Using a Desirability Function and a Genetic Algorithm

The use of compound biological fingerprints built on data from high-throughput screening (HTS) campaigns, or HTS fingerprints, is a novel cheminformatics method of representing compounds by integrating chemical and biological activity data that is gaining momentum in its application to drug discovery, including hit expansion, target identification, and virtual screening. HTS fingerprints present two major limitations, noise and missing data, which are intrinsic to the high-throughput data acquisition technologies and to the assay availability or assay selection procedure used for their construction. In this work, we present a methodology to define an optimal set of HTS fingerprints by using a desirability function that encodes the principles of maximum biological and chemical space coverage and minimum redundancy between HTS assays. We used a genetic algorithm to optimize the desirability function and obtained an optimal fingerprint that was evaluated for performance in a test set of 33 diverse assays. Our results show that the optimal HTS fingerprint represents compounds in chemical biology space using 25% fewer assays. When used for virtual screening, the optimal HTS fingerprint obtained equivalent performance, in terms of both area under the curve and enrichment factors, to full fingerprints for 27 out of 33 test assays, while randomly assembled fingerpints could achieve equivalent performance in only 23 test assays

Optimal HTS Fingerprint Definitions by Using a Desirability Function and a Genetic Algorithm

The use of compound biological fingerprints built on data from high-throughput screening (HTS) campaigns, or HTS fingerprints, is a novel cheminformatics method of representing compounds by integrating chemical and biological activity data that is gaining momentum in its application to drug discovery, including hit expansion, target identification, and virtual screening. HTS fingerprints present two major limitations, noise and missing data, which are intrinsic to the high-throughput data acquisition technologies and to the assay availability or assay selection procedure used for their construction. In this work, we present a methodology to define an optimal set of HTS fingerprints by using a desirability function that encodes the principles of maximum biological and chemical space coverage and minimum redundancy between HTS assays. We used a genetic algorithm to optimize the desirability function and obtained an optimal fingerprint that was evaluated for performance in a test set of 33 diverse assays. Our results show that the optimal HTS fingerprint represents compounds in chemical biology space using 25% fewer assays. When used for virtual screening, the optimal HTS fingerprint obtained equivalent performance, in terms of both area under the curve and enrichment factors, to full fingerprints for 27 out of 33 test assays, while randomly assembled fingerpints could achieve equivalent performance in only 23 test assays

Aggregated compound biological signatures facilitate phenotypic drug discovery and target elucidation

Predicting the cellular response of compounds is a challenge central to the discovery of new drugs. Compound biological signatures have risen as a way of representing the perturbation produced by a compound in the cell. However, their ability to encode specific phenotypic information and generating tangible predictions remains unknown, mainly because of the inherent noise in such data sets. In this work, we statistically aggregate signals from several compound biological signatures to find compounds that produce a desired phenotype in the cell. We exploit this method in two applications relevant for phenotypic screening in drug discovery programs: target-independent hit expansion and target identification. As a result, we present here (i) novel nanomolar inhibitors of cellular division that reproduce the phenotype and the mode of action of reference natural products and (ii) blockers of the NKCC1 cotransporter for autism spectrum disorders. Our results were confirmed in both cellular and biochemical assays of the respective projects. In addition, these examples provided novel insights on the information content and biological significance of compound biological signatures from HTS, and their applicability to drug discovery in general. For target identification, we show that novel targets can be predicted successfully for drugs by reporting new activities for nimedipine, fluspirilene, and pimozide and providing a rationale for repurposing and side effects. Our results highlight the opportunities of reusing public bioactivity data for prospective drug discovery, including scenarios where the effective target or mode of action of a particular molecule is not known, such as in phenotypic screening campaigns.A.C.C. and P.M.P. gratefully acknowledge the Roche Postdoctoral Fellowship Program for financial support. The authors thank especially Martin Erkens, Rene Wyler, Thilo Enderle, Lisa Sach-Peltason, Samuel Croset, and Oliv Eidam for their help and thoughtful comments and Klaus Muller for his support and invaluable insight. Eurofins’ (CEREP) is gratefully acknowledged for providing us with single concentration activities from the BioPrint database in advantageous conditions. F.D., D.L.A., M.R.H., and I.B. acknowledge support from grants BIO2013-42984-R (Ministerio de Economia y Competitividad) and S2010/BMD-2457 BIPEDD2 (Comunidad Autónoma de Madrid (JFD))

Identification of NEK3 and MOK as novel targets for lithium

Lithium ion, commonly used as the carbonate salt in the treatment of bipolar disorders, has been identified as an inhibitor of several kinases, including Glycogen Synthase Kinase-3β, for almost 20 years. However, both the exact mechanism of enzymatic inhibition and its apparent specificity for certain metalloenzymes are still a matter of debate. A data-driven hypothesis is presented that accounts for the specificity profile of kinase inhibition by lithium in terms of the presence of a unique protein environment in the magnesium-binding site. This hypothesis has been validated by the discovery of two novel potential targets for lithium, namely NEK3 and MOK, which are related to neuronal function.Ministerio de Educación, Cultura y Deporte, Grant/Award Number: SAF2015-64629-C2-2-RPeer Reviewe

Aggregated Compound Biological Signatures Facilitate Phenotypic Drug Discovery and Target Elucidation

Predicting the cellular response of compounds is a challenge central to the discovery of new drugs. Compound biological signatures have risen as a way of representing the perturbation produced by a compound in the cell. However, their ability to encode specific phenotypic information and generating tangible predictions remains unknown, mainly because of the inherent noise in such data sets. In this work, we statistically aggregate signals from several compound biological signatures to find compounds that produce a desired phenotype in the cell. We exploit this method in two applications relevant for phenotypic screening in drug discovery programs: target-independent hit expansion and target identification. As a result, we present here (i) novel nanomolar inhibitors of cellular division that reproduce the phenotype and the mode of action of reference natural products and (ii) blockers of the NKCC1 cotransporter for autism spectrum disorders. Our results were confirmed in both cellular and biochemical assays of the respective projects. In addition, these examples provided novel insights on the information content and biological significance of compound biological signatures from HTS, and their applicability to drug discovery in general. For target identification, we show that novel targets can be predicted successfully for drugs by reporting new activities for nimedipine, fluspirilene, and pimozide and providing a rationale for repurposing and side effects. Our results highlight the opportunities of reusing public bioactivity data for prospective drug discovery, including scenarios where the effective target or mode of action of a particular molecule is not known, such as in phenotypic screening campaigns

Aggregated Compound Biological Signatures Facilitate Phenotypic Drug Discovery and Target Elucidation

Predicting the cellular response of compounds is a challenge central to the discovery of new drugs. Compound biological signatures have risen as a way of representing the perturbation produced by a compound in the cell. However, their ability to encode specific phenotypic information and generating tangible predictions remains unknown, mainly because of the inherent noise in such data sets. In this work, we statistically aggregate signals from several compound biological signatures to find compounds that produce a desired phenotype in the cell. We exploit this method in two applications relevant for phenotypic screening in drug discovery programs: target-independent hit expansion and target identification. As a result, we present here (i) novel nanomolar inhibitors of cellular division that reproduce the phenotype and the mode of action of reference natural products and (ii) blockers of the NKCC1 cotransporter for autism spectrum disorders. Our results were confirmed in both cellular and biochemical assays of the respective projects. In addition, these examples provided novel insights on the information content and biological significance of compound biological signatures from HTS, and their applicability to drug discovery in general. For target identification, we show that novel targets can be predicted successfully for drugs by reporting new activities for nimedipine, fluspirilene, and pimozide and providing a rationale for repurposing and side effects. Our results highlight the opportunities of reusing public bioactivity data for prospective drug discovery, including scenarios where the effective target or mode of action of a particular molecule is not known, such as in phenotypic screening campaigns

Aggregated Compound Biological Signatures Facilitate Phenotypic Drug Discovery and Target Elucidation

Predicting the cellular response of compounds is a challenge central to the discovery of new drugs. Compound biological signatures have risen as a way of representing the perturbation produced by a compound in the cell. However, their ability to encode specific phenotypic information and generating tangible predictions remains unknown, mainly because of the inherent noise in such data sets. In this work, we statistically aggregate signals from several compound biological signatures to find compounds that produce a desired phenotype in the cell. We exploit this method in two applications relevant for phenotypic screening in drug discovery programs: target-independent hit expansion and target identification. As a result, we present here (i) novel nanomolar inhibitors of cellular division that reproduce the phenotype and the mode of action of reference natural products and (ii) blockers of the NKCC1 cotransporter for autism spectrum disorders. Our results were confirmed in both cellular and biochemical assays of the respective projects. In addition, these examples provided novel insights on the information content and biological significance of compound biological signatures from HTS, and their applicability to drug discovery in general. For target identification, we show that novel targets can be predicted successfully for drugs by reporting new activities for nimedipine, fluspirilene, and pimozide and providing a rationale for repurposing and side effects. Our results highlight the opportunities of reusing public bioactivity data for prospective drug discovery, including scenarios where the effective target or mode of action of a particular molecule is not known, such as in phenotypic screening campaigns

Aggregated Compound Biological Signatures Facilitate Phenotypic Drug Discovery and Target Elucidation

Predicting the cellular response of compounds is a challenge central to the discovery of new drugs. Compound biological signatures have risen as a way of representing the perturbation produced by a compound in the cell. However, their ability to encode specific phenotypic information and generating tangible predictions remains unknown, mainly because of the inherent noise in such data sets. In this work, we statistically aggregate signals from several compound biological signatures to find compounds that produce a desired phenotype in the cell. We exploit this method in two applications relevant for phenotypic screening in drug discovery programs: target-independent hit expansion and target identification. As a result, we present here (i) novel nanomolar inhibitors of cellular division that reproduce the phenotype and the mode of action of reference natural products and (ii) blockers of the NKCC1 cotransporter for autism spectrum disorders. Our results were confirmed in both cellular and biochemical assays of the respective projects. In addition, these examples provided novel insights on the information content and biological significance of compound biological signatures from HTS, and their applicability to drug discovery in general. For target identification, we show that novel targets can be predicted successfully for drugs by reporting new activities for nimedipine, fluspirilene, and pimozide and providing a rationale for repurposing and side effects. Our results highlight the opportunities of reusing public bioactivity data for prospective drug discovery, including scenarios where the effective target or mode of action of a particular molecule is not known, such as in phenotypic screening campaigns