BIGL : Biochemically Intuitive Generalized Loewe null model for prediction of the expected combined effect compatible with partial agonism and antagonism

Clinical efficacy regularly requires the combination of drugs. For an early estimation of the clinical value of (potentially many) combinations of pharmacologic compounds during discovery, the observed combination effect is typically compared to that expected under a null model. Mechanistic accuracy of that null model is not aspired to; to the contrary, combinations that deviate favorably from the model (and thereby disprove its accuracy) are prioritized. Arguably the most popular null model is the Loewe Additivity model, which conceptually maps any assay under study to a (virtual) single-step enzymatic reaction. It is easy-to-interpret and requires no other information than the concentration-response curves of the individual compounds. However, the original Loewe model cannot accommodate concentration-response curves with different maximal responses and, by consequence, combinations of an agonist with a partial or inverse agonist. We propose an extension, named Biochemically Intuitive Generalized Loewe (BIGL), that can address different maximal responses, while preserving the biochemical underpinning and interpretability of the original Loewe model. In addition, we formulate statistical tests for detecting synergy and antagonism, which allow for detecting statistically significant greater/lesser observed combined effects than expected from the null model. Finally, we demonstrate the novel method through application to several publicly available datasets

Industry-scale application and evaluation of deep learning for drug target prediction

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.Web of Science121art. no. 2

High-resolution profiling of the LEDGF/p75 chromatin interaction in the ENCODE region

Lens epithelium-derived growth factor/p75 (LEDGF/p75) is a transcriptional coactivator involved in stress response, autoimmune disease, cancer and HIV replication. A fusion between the nuclear pore protein NUP98 and LEDGF/p75 has been found in human acute and chronic myeloid leukemia and association of LEDGF/p75 with mixed-lineage leukemia (MLL)/menin is critical for leukemic transformation. During lentiviral replication, LEDGF/p75 tethers the pre-integration complex to the host chromatin resulting in a bias of integration into active transcription units (TUs). The consensus function of LEDGF/p75 is tethering of cargos to chromatin. In this regard, we determined the LEDGF/p75 chromatin binding profile. To this purpose, we used DamID technology and focused on the highly annotated ENCODE (Encyclopedia of DNA Elements) regions. LEDGF/p75 primarily binds downstream of the transcription start site of active TUs in agreement with the enrichment of HIV-1 integration sites at these locations. We show that LEDGF/p75 binding is not restricted to stress response elements in the genome, and correlation analysis with more than 200 genomic features revealed an association with active chromatin markers, such as H3 and H4 acetylation, H3K4 monomethylation and RNA polymerase II binding. Interestingly, some associations did not correlate with HIV-1 integration indicating that not all LEDGF/p75 complexes on the chromosome are amenable to HIV-1 integration

Industry-Scale Orchestrated Federated Learning for Drug Discovery

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.Comment: 9 pages, 4 figures, to appear in AAAI-23 ([IAAI-23 track] Deployed Highly Innovative Applications of AI

PP1γ2 and PPP1R11 Are Parts of a Multimeric Complex in Developing Testicular Germ Cells in which their Steady State Levels Are Reciprocally Related

Mice lacking the protein phosphatase 1 gamma isoforms, PP1γ1 and PP1γ2, are male-sterile due to defective germ cell morphogenesis and apoptosis. However, this deficiency causes no obvious abnormality in other tissues. A biochemical approach was employed to learn how expression versus deficiency of PP1γ2, the predominant PP1 isoform in male germ cells, affects spermatogenesis. Methods used in this study include column chromatography, western blot and northern blot analyses, GST pull-down assays, immunoprecipitation, non-denaturing gel electrophoresis, phosphatase enzyme assays, protein sequencing, and immunohistochemistry. We report for the first time that in wild-type testis, PP1γ2 forms an inactive complex with actin, protein phosphatase 1 regulatory subunit 7 (PPP1R7), and protein phosphatase 1 regulatory subunit 11 (PPP1R11), the latter, a potent PP1 inhibitor. Interestingly, PPP1R11 protein, but not its mRNA level, falls significantly in PP1γ-null testis where mature sperm are virtually absent. Conversely, both mature sperm numbers and the PPP1R11 level increase substantially in PP1γ-null testis expressing transgenic PP1γ2. PPP1R11 also appears to be ubiquitinated in PP1γ-null testis. The levels of PP1γ2 and PPP1R11 were increased in phenotypically normal PP1α-null testis. However, in PP1α-null spleen, where PP1γ2 normally is not expressed, PPP1R11 levels remained unchanged. Our data clearly show a direct reciprocal relationship between the levels of the protein phosphatase isoform PP1γ2 and its regulator PPP1R11, and suggest that complex formation between these polypeptides in testis may prevent proteolysis of PPP1R11 and thus, germ cell apoptosis