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The Dog as a Second Species for Toxicology Testing Provides Value to Drug Development.
No full textThe objective of the pharmaceutical industry is to develop new drugs that are safe for human use. In many cases, the accepted approach codified in guidance from regulatory authorities to assess the nonclinical safety profile of potential pharmaceuticals is to perform toxicity testing in two species. However, the use of a second species to establish the safety of new pharmaceuticals has been the subject of much scrutiny in recent years and the industry has been repeatedly challenged to reduce, refine, or replace some or all of the animals used to establish the safety of these pharmaceutical candidates. Specifically, the value of the dog in this testing paradigm has been questioned. Publications reviewing available data for marketed drugs suggest that for many drugs, the dog does not identify unique toxicities critical to human safety. The weakness of this approach, however, is that many of the cases where the dog (or any other species) has the greatest impact on drug development are cases for which development decisions based on safety concerns are not shared publicly. The European Federation of Pharmaceutical Industries and Associations (EFPIA) Preclinical Development Expert Group (PDEG) decided to share case studies collected from its membership and the literature to illustrate the value of the dog in drug development decision-making and clinical monitoring practices to protect the safety of trial subjects
Predicting in vivo brain penetration using multitask graph neural networks
No full textThe blood-brain-barrier (BBB) is a semi-permeable interface, separating the central nervous system (CNS) from the blood stream. The physiological role of the BBB is to create a stable microenvironment for the CNS by tightly regulating the transport of molecules from the blood to the brain and vice versa. For early drug discovery teams, it can be critical to know if com-pounds are able to penetrate into the brain compartment. Generally, pre-clinical in vivo studies measuring the ratio of total and free brain and blood concentrations (Kp and Kpuu, respectively) are required to estimate the brain penetration potential of a new drug entitiy. In this work, we evaluated the performance of different machine learning approaches to predict Kp, using Novar-tis internal and publicly available experimental data. We investigated the benefit of including in vitro experimental data as auxiliary tasks in multitask graph neural network (MT-GNN) mod-els. We observed that MT-GNN models generally outperformed single-task (ST) learning ap-proaches, which were only trained on in vivo brain penetration data. The best performing MT-GNN regression model achieved a coefficient of determination (R2) of 0.42 on a prospective validation set and outperformed all tested ST models. Overall, models solely based on public data achieved lower performance on the prospective validation set compared to models built with internal data. However, the MT-GNN based upon literature data outperformed all litera-ture-based ST models, with a R2 of 0.31. Lastly, we observed that post hoc classification using a MT-GNN regression model outperformed a MT-GNN classification model, with Matthew’s correlation coefficient values of 0.66 and 0.44, respectively. Taken together, we show that the inclusion of the right auxiliary tasks improves the prediction of in vivo brain penetration using MT-GNNs
The function of 14-3-3 protein family and its connection with cancer review
No full text14-3-3 is a family of conserved acidic proteins. The 14-3-3 proteins are key modulators of
more than 200 proteins and these interactions occur by either a phosphorylation-dependant
or phosphorylation-independent manner. 14-3-3 target proteins are implicated in key
signalling pathways that control the cellular physiology including apoptosis, cell cycle,
differentiation, proliferation, and migration. Their important role in maintaining genome
integrity and correct cell cycle progression exemplifies how de-regulated 14-3-3 expression
and activity is an important event in cancer. Better understanding the complex interactions
14-3-3 have with their targets gives us new approaches in targeted cancer therapies
Structural basis for context-specific inhibition of translation by oxazolidinone antibiotics.
No full textThe antibiotic linezolid, the first clinically approved member of the oxazolidinone class, inhibits translation of bacterial ribosomes by binding to the peptidyl transferase center. Recent work has demonstrated that linezolid does not inhibit peptide bond formation at all sequences but rather acts in a context-specific manner, namely when alanine occupies the penultimate position of the nascent chain. However, the molecular basis for context-specificity has not been elucidated. Here we show that the second-generation oxazolidinone radezolid also induces stalling with a penultimate alanine, and we determine high-resolution cryo-EM structures of linezolid- and radezolid-stalled ribosome complexes to explain their mechanism of action. These structures reveal that the alanine side chain fits within a small hydrophobic crevice created by oxazolidinone, resulting in improved ribosome binding. Modification of the ribosome by the antibiotic resistance enzyme Cfr disrupts stalling due to repositioning of the modified nucleotide. Together, our findings provide molecular understanding for the context-specificity of oxazolidinones
Liver enzyme delayed clearance in rat treated by CSF1 receptor specific antagonist Sotuletinib.
No full textSotuletinib (BLZ945), a CSF1-R specific kinase inhibitor developed for the treatment of Amyotrophic Lateral Sclerosis, induced liver enzyme elevation in absence of hepatic lesions in preclinical rat and monkey studies. The depletion of Kupffer cells through CSF1 pathway inhibition has been hypothesized as responsible for this effect. However, a release of these biomarkers from unseen hepatic lesions or from other organs cannot be excluded. Here we demonstrate a delayed clearance independently of any potential organ lesions by exogenously injecting recombinant his-Tagged ALT1 into rats pretreated with Sotuletinib, excluding a potential undetected cytotoxic effect
Industry Perspective on the Use and Characterization of Polysorbates for Biopharmaceutical Products Part 2: Survey Report on Control Strategy Preparing for the Future.
No full textPolysorbate (PS) 20 and 80 are the main surfactants used to stabilize biopharmaceutical products. Industry practices on various aspects of PS based on a confidential survey and following discussions by 16 globally acting major biotechnology companies is presented in two publications. Part 1 summarizes the current practice and use of PS during manufacture in addition to aspects like current understanding of the (in)stability of PS, the routine QC testing and control of PS, and selected regulatory aspects of PS.1 The current part 2 of the survey focusses on understanding, monitoring, prediction, and mitigation of PS degradation pathways in order to propose an effective control strategy. The results of the survey and extensive cross-company discussions are put into relation with currently available scientific literature
An Activity-Based Oxaziridine Platform for Developing Covalent Ligands Against Functional Allosteric Methionine Sites: Redox-Dependent Inhibition of Cyclin-Dependent Kinase 4
No full textActivity-based protein profiling (ABPP) is a versatile strategy for enabling identification and characterization of new functional protein sites and discovery of lead compounds for therapeutic development. Yet, the vast majority of ABPP methods applied for covalent drug discovery target highly nucleophilic amino acids such as cysteine or lysine. Here, we report a methionine-directed ABPP platform using Redox-Activated Chemical Tagging (ReACT), which leverages a biomimetic oxidative ligation strategy for selective methionine modification. Application of ReACT to the cancer-driver protein cyclin-dependent kinase 4 (CDK4) as a representative high-value drug target identified three new hyperreactive, ligandable methionine residues, including an allosteric M169 site that is proximal to an activating T172 phosphorylation site. With this information in hand, we designed and synthesized a new methionine-targeting covalent ligand library based on oxaziridine fragments bearing a diverse array of heterocyclic, heteroatom, and stereochemically-rich substituents. ABPP screening of this focused library against a clickable broad-spectrum ReACT probe identified 1oxF11 as a covalent modifier of the CDK4/Cyclin-D1 heterodimer at the M169 site. This compound inhibited CDK4 kinase activity in a dose-dependent manner on purified protein and in live cells. Further biochemical analyses with a phospho-specific CDK4 antibody revealed crosstalk between M169 oxidation and T172 phosphorylation upon 1oxF11 treatment, where M169 oxidation prevented phosphorylation of the activating T172 site on CDK4 and blocked cell cycle progression at the S-phase checkpoint. By identifying a new mechanism for allosteric methionine redox regulation on CDK4 and developing a unique modality for its therapeutic intervention, this work showcases a generalizable platform that provides a starting point for engaging in broader chemoproteomics and protein ligand discovery efforts to find and target previously undruggable methionine sites
Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population.
No full textType 2 diabetes (T2D) has a heterogeneous etiology influencing its progression, treatment, and complications. A data driven cluster analysis in European individuals with T2D previously identified four subtypes: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes. Here, the clustering approach was applied to individuals with T2D from the Qatar Biobank and validated in an independent set. Cluster-specific signatures of circulating metabolites and proteins were established, revealing subtype-specific molecular mechanisms, including activation of the complement system with features of autoimmune diabetes and reduced 1,5-anhydroglucitol in SIDD, impaired insulin signaling in SIRD, and elevated leptin and fatty acid binding protein levels in MOD. The MARD cluster was the healthiest with metabolomic and proteomic profiles most similar to the controls. We have translated the T2D subtypes to an Arab population and identified distinct molecular signatures to further our understanding of the etiology of these subtypes
Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design
No full textDevelopment and optimization of biopharmaceutical production processes with cell cultures is cost- and time-consuming and often performed rather empirically. Efficient optimization of multiple-objectives like process time, viable cell density, number of operating steps & cultivation scales, required medium, amount of product as well as product quality depicts a promising approach. This contribution presents a workflow which couples uncertainty-based upstream simulation and Bayes optimization using Gaussian processes. Its application is demonstrate in a simulation case study for a relevant industrial task in process development, the design of a robust cell culture expansion process (so called seed train), meaning that despite uncertainties and variabilities concerning cell growth, low variations of viable cell density during the seed train are obtained. Compared to a non-optimized reference seed train, the optimized process showed much lower deviation rates regarding viable cell densities (< 10% instead of 41.7%) using 5 or 4 shake flask scales and seed train duration could be reduced by 56 h from 576 h to 520 h. Overall, it is shown that applying Bayes optimization allows for optimization of a multi-objective optimization function with several optimizable input variables and under a considerable amount of constraints with a low computational effort. This approach provides the potential to be used in form of a decision tool, e.g. for the choice of an optimal and robust seed train design or for further optimization tasks within process development
Unsupervised machine-learning algorithms for the identification of clinical phenotypes in the osteoarthritis initiative database.
No full textOsteoarthritis (OA) is a complex disease comprising diverse underlying patho-mechanisms. To enable the development of effective therapies, segmentation of the heterogenous patient population is critical. This study aimed at identifying such patient clusters using two different machine learning algorithms.Using the progression and incident cohorts of the Osteoarthritis Initiative (OAI) dataset, deep embedded clustering (DEC) and multiple factor analysis with clustering (MFAC) approaches, including 157 input-variables at baseline, were employed to differentiate specific patient profiles.DEC resulted in 5 and MFAC in 3 distinct patient phenotypes. Both identified a "comorbid" cluster with higher body mass index (BMI), relevant burden of comorbidity and low levels of physical activity. Both methods also identified a younger and physically more active cluster and an elderly cluster with functional limitations, but low disease impact. The additional two clusters identified with DEC were subgroups of the young/physically active and the elderly/physically inactive clusters. Overall pain trajectories over 9 years were stable, only the numeric rating scale (NRS) for pain showed distinct increase, while physical activity decreased in all clusters. Clusters showed different (though non-significant) trajectories of joint space changes over the follow-up period of 8 years.Two different clustering approaches yielded similar patient allocations primarily separating complex "comorbid" patients from healthier subjects, the latter divided in young/physically active vs elderly/physically inactive subjects. The observed association to clinical (pain/physical activity) and structural progression could be helpful for early trial design as strategy to enrich for patients who may specifically benefit from disease-modifying treatments