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Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery.
No full textWe present a user-friendly molecular generative pipeline called Pocket Crafter, specifically designed to facilitate hit finding activity in the drug discovery process. This workflow utilized a three-dimensional (3D) generative modeling method Pocket2Mol, for the de novo design of molecules in spatial perspective for the targeted protein structures, followed by filters for chemical-physical properties and drug-likeness, structure-activity relationship analysis, and clustering to generate top virtual hit scaffolds. In our WDR5 case study, we acquired a focused set of 2029 compounds after a targeted searching within Novartis archived library based on the virtual scaffolds. Subsequently, we experimentally profiled these compounds, resulting in a novel chemical scaffold series that demonstrated activity in biochemical and biophysical assays. Pocket Crafter successfully prototyped an effective end-to-end 3D generative chemistry-based workflow for the exploration of new chemical scaffolds, which represents a promising approach in early drug discovery for hit identification
Industry perspective on a holistic container closure integrity approach to parenteral combination products.
No full textBiologics are being developed more and more as parenteral combination products with drug delivery devices. The maintenance of sterility is imperative for such medical devices throughout their life cycle. Therefore, the container closure integrity (CCI) should, preferably, be built into the overall process, and not just demonstrated during the final testing of the combination product. The integrity is an important Critical Quality Attribute (CQA) and in the scope of specific considerations and studies during the combination product life cycle i.e., design robustness, assembly processes, storage (to end of shelf life), and shipping prior to patient use. The goal of this paper is to summarize an industry holistic approach to ensure CCI, for a combination product, and to build a scientifically based justification that Quality (in terms of CCI) is built into the overall process. Current analytical approaches used for characterization or Good Manufacturing Practice (GMP) CCI testing during combination product development will be described. However, the use of quality by design (QbD) during product development can reduce or eliminate routine batch level or stability testing of the combination product
Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study.
No full textWrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies.The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back-worn inertial sensors.Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back-worn inertial sensors.The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids.Algorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health.ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987.RR2-10.1136/bmjopen-2021-050785
Drug target prediction through deep learning functional representation of gene signatures.
No full textMany machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institute's L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts
Compound activity prediction with dose-dependent transcriptomic profiles and deep learning
No full textPredicting compound activity in assays is a long-standing challenge in drug discovery. Computational models based on compound-induced gene-expression signatures from a single profiling assay have shown promise towards predicting compound activity in other, seemingly unrelated, assays. Applications of such models include predicting mechanisms-of-action (MoA) for phenotypic hits, identifying off-target activities, and identifying polypharmacologies. Here, we introduce Transcriptomics-to-Activity Transformer (TAT) models that leverage gene-expression profiles observed over compound treatment at multiple concentrations to predict compound activity in other biochemical or cellular assays. We built TAT models based on gene-expression data from a RASL-Seq assay to predict the activity of 2,692 compounds in 262 dose response assays. We obtained useful models for 51% of the assays as determined through a realistic held-out set. Prospectively, we experimentally validated the activity predictions of a TAT model in a malaria inhibition assay. With a 63% hit rate, TAT successfully identified several sub-micromolar malaria inhibitors. Our results thus demonstrate the potential of transcriptomic responses over compound concentration and the TAT modeling framework as a cost-efficient way to identify the bioactivities of promising compounds across many assays
HDAC6/aggresome processing pathway importance for inflammasome formation is context-dependent.
No full textThe inflammasome is a large multiprotein complex that assembles in the cell cytoplasm in response to stress or pathogenic infection. Its primary function is to defend the cell and promote the secretion of pro-inflammatory cytokines, including IL-1β and IL-18. Previous research has shown that in immortalized bone marrow-derived macrophages (iBMDMs) inflammasome assembly is dependent on the deacetylase HDAC6 and the aggresome processing pathway (APP), a cellular pathway involved in the disposal of misfolded proteins. Here we used primary BMDMs from mice in which HDAC6 is ablated or impaired and found that inflammasome activation was largely normal. We also used human peripheral blood mononuclear cells and monocyte cell lines expressing a synthetic protein blocking the HDAC6-ubiquitin interaction and impairing the APP and found that inflammasome activation was moderately affected. Finally, we used a novel HDAC6 degrader and showed that inflammasome activation was partially impaired in human macrophage cell lines with depleted HDAC6. Our results therefore show that HDAC6 importance in inflammasome activation is context-dependent
Prenatal administration of AAV9-GFP in fetal lambs results in widespread distribution into brain and spinal cord and transduction of female germ cells
No full textPrenatal somatic cell gene therapy could potentially treat severe, early-onset genetic disorders such as spinal muscular atrophy or muscular dystrophy. Given the current clinical use of adeno-associated virus serotype 9 (AAV9) vectors, we tested the safety and biodistribution of AAV9-GFP in fetal lambs after umbilical vein (UV) or intracranial (IC) injection at embryonic day 75 (E75) and harvest at E96 or E140 (term=E150). We detected widespread biodistribution of vector genomes in all examined lamb tissues and in maternal uteruses. There was robust GFP expression in brain, spinal cord, dorsal root ganglia (DRGs), with no evident DRG toxicity. There was excellent transduction of diaphragm and quadriceps muscles. However, we noted several findings indicative of systemic toxicity including growth restriction and transient elevation of total bilirubin (in ewes and lambs). There were no antibodies against GFP but there were elevations in anti-AAV9 antibodies in ewes and lambs. Analysis of fetal gonads demonstrated GFP expression in female (but not male) germ cells, with a low level of integration-specific reads , without integration in protooncogenes . These results support using the sheep model for further preclinical development and suggest potential therapeutic benefit for neuromuscular disorders; however, observed toxicities, particularly transduction of female germ cells, warrant caution
DCAF1-based PROTACs with activity against clinically validated targets overcoming intrinsic- and acquired-degrader resistance.
No full textTargeted protein degradation (TPD) mediates protein level through small molecule induced redirection of E3 ligases to ubiquitinate neo-substrates and mark them for proteasomal degradation. TPD has recently emerged as a key modality in drug discovery. So far only a few ligases have been utilized for TPD. Interestingly, the workhorse ligase CRBN has been observed to be downregulated in settings of resistance to immunomodulatory inhibitory drugs (IMiDs). Here we show that the essential E3 ligase receptor DCAF1 can be harnessed for TPD utilizing a selective, non-covalent DCAF1 binder. We confirm that this binder can be functionalized into an efficient DCAF1-BRD9 PROTAC. Chemical and genetic rescue experiments validate specific degradation via the CRL4DCAF1 E3 ligase. Additionally, a dasatinib-based DCAF1 PROTAC successfully degrades cytosolic and membrane-bound tyrosine kinases. A potent and selective DCAF1-BTK-PROTAC (DBt-10) degrades BTK in cells with acquired resistance to CRBN-BTK-PROTACs while the DCAF1-BRD9 PROTAC (DBr-1) provides an alternative strategy to tackle intrinsic resistance to VHL-degrader, highlighting DCAF1-PROTACS as a promising strategy to overcome ligase mediated resistance in clinical settings
Defensive tolerance results in dysfunction of infiltrating cytotoxic CD8+ T cells through in situ reprogramming
No full textTolerance of mouse kidney allografts arises in grafts that develop regulatory Tertiary
Lymphoid Organs (rTLOs). scRNAseq data and adoptive transfer of alloreactive T cells posttransplant
show that cytotoxic CD8+ T cells are reprogrammed within the accepted graft to an
exhausted/regulatory-like phenotype. Establishment of rTLOs is required since adoptive transfer
of alloreactive T cells prior to transplantation results in kidney allograft rejection. Despite
intragraft CD8+ cells with a regulatory phenotype, they were not essential for the induction and
maintenance of kidney allograft tolerance. Analysis of scRNAseq data from allograft kidneys
and malignant tumors identified similar regulatory-like cell types within the T cell clusters.
Induction of cytotoxic CD8+ T cell dysfunction of infiltrating cells appears to be a beneficial
mechanistic pathway that protects the kidney allotransplant from rejection through a process we
call “defensive tolerance.” This novel pathway has implications for our understanding of
allotransplant tolerance and tumor resistance to host immunity
Ligandability Assessment of IL-1β by Integrated Hit Identification Approaches.
No full textHuman interleukin-1β (IL-1β) is a pro-inflammatory cytokine that plays a critical role in the regulation of the immune response and the development of various inflammatory diseases. In this publication, we disclose our efforts toward the discovery of IL-1β binders that interfere with IL-1β signaling. To this end, several technologies were used in parallel, including fragment-based screening (FBS), DNA-encoded library (DEL) technology, peptide discovery platform (PDP), and virtual screening. The utilization of distinct technologies resulted in the identification of new chemical entities exploiting three different sites on IL-1β, all of them also inhibiting the interaction with the IL-1R1 receptor. Moreover, we identified lysine 103 of IL-1β as a target residue suitable for the development of covalent, low-molecular-weight IL-1β antagonists