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Anti-Langmuir elution behavior of a bispecific monoclonal antibody in cation exchange chromatography: Mechanistic modeling using a pH-dependent Self-Association Steric Mass Action isotherm
No full textThe objective of this scientific work was to model and simulate the complex anti-Langmuir elution behavior of a bispecific monoclonal antibody (bsAb) under high loading conditions on the strong cation exchange resin POROS™ XS. The bsAb exhibited anti-Langmuirian elution behavior as a consequence of self-association expressed both in uncommon retentions and peak shapes highly atypical for antibodies. The widely applied Steric Mass Action (SMA) model was unsuitable here because it can only describe Langmuirian elution behavior and is not able to describe protein-protein interactions in the form of self-association. For this reason, a Self-Association SMA (SAS-SMA) model was applied, which was extended by two activity coefficients for the salt and protein in solution. This model is able to describe protein-protein interactions in the form of self-dimerization and thus can describe anti-Langmuir elution behavior. Linear gradient elution (LGE) experiments were carried out to obtain a broad dataset ranging from pH 4.5 to 7.3 and from 50 to 375 mmol/L Na+ for model parameter determination. High loading LGE experiments were conducted with an increasing load from 0.5 up to 75.0 mgbsAb/mLresin. Thereby, pH-dependent empirical correlations for the activity coefficient of the solute protein, for the equilibrium constant of the self-dimerization process and for the shielding factor could be set up and ultimately incorporated into the SAS-SMA model. This pH-dependent SAS-SMA model was thus able to simulate anti-Langmuir behavior over extended ranges of pH, counterion concentration, and column loading. The model was confirmed by experimental verification of simulated linear pH gradient elutions up to a load of 75.0 mgbsAb/mLresin
Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient derived xenografts and patient tumors using deep neural networks
No full textCell lines and patient-derived xenografts are essential to cancer research, however, the results derived from such models often lack clinical translatability, primarily due to the fact that these models do not fully recapitulate the complex cancer biology. It is therefore critically important to better understand the systematic differences between cell lines, xenografts and clinical tumors, and to be able to identify pre-clinical models that sufficiently resemble the biological characteristics of clinical tumors across different cancers. On another side, direct comparison of transcriptional profiles from pre-clinical models and clinical tumors is infeasible due to the mixture of technical artifacts and inherent biological signals.
To address these challenges, we developed MOBER, Multi-Origin Batch Effect Removal method, to simultaneously extract biologically meaningful embeddings and remove batch effects from transcriptomic datasets of different origin. MOBER consists of two neural networks: conditional variational autoencoder and source discriminator neural network that is trained in adversarial fashion. We applied MOBER on transcriptional profiles from 932 cancer cell lines, 434 patient-derived xenografts and 11159 clinical tumors and identified pre-clinical models with greatest transcriptional fidelity to clinical tumors, and models that are transcriptionally unrepresentative of their respective clinical tumors. We demonstrate that MOBER can conserve the biological signals from the original datasets, while generating embeddings that do not encode confounder information. In addition, it allows for transformation of transcriptional profiles of pre-clinical models into clinical tumors and we show how it can be used to improve the clinical translation of insights gained from pre-clinical models.
As a batch effect removal method, MOBER confers superior performance over the state-of-the-art methods, while allowing for integration of multiple datasets simultaneously
Toxicokinetics in preclinical drug development of small-molecule new chemical entities.
No full textToxicokinetics (TK) is an integral part of nonclinical (preclinical) safety assessment of small-molecule new chemical entities in drug development. It is employed to describe the systemic exposure of a drug candidate and/or its important metabolite(s) achieved in study animals and elucidate the relationship (proportional, over-proportional, or under-proportional) between systemic exposure and dose administered and the associated differences/similarities between male and female animals along with the possible accumulation/induction. TK data and the derived parameters are employed to propose safe starting doses for clinical use of the new drug candidate through proper extrapolation of findings in study animals to humans. This review has attempted to highlight the health authority expectations on TK assessment in supporting preclinical safety profiling of new chemical entities. A robust TK assessment requires good understanding of absorption, distribution, metabolism, and elimination processes of drug candidate, adequate TK sampling (e.g., controls where relevant), implementation of fit-for-purpose bioanalytical methods (validated or scientifically qualified) along with necessary measures to prevent mis-dosing or ex vivo contamination, and establishment of stability of the drug candidate and/or its metabolite(s) in the intended species matrix to ensure the reliability of bioanalytical and TK data. The latter provides a vital link between animal experiments and human safety
A whole genome scan for Artemisinin cytotoxicity reveals a novel therapy for human brain cancer
No full textThe natural medical compound Artemisinin is the most widely used anti-malarial drug worldwide. Based on its cytotoxic activity, it is also being used for anti-cancer therapy. Artemisinin is an endoperoxide that damages proteins in eukaryotic cells; its definite mechanism of action and critical host cell targets, however, have remained largely elusive. Using hip-hop yeast profiling and haploid ES cell screening, we demonstrate that only a single cellular pathway, namely (mitochondrial) porphyrin or heme biosynthesis, is accountable for Artemisinin’s cytotoxicity. Genetic or pharmacological modulation of porphyrin production is sufficient to alter the cytotoxic activity of Artemisinin in multiple eukaryotic cells, including human cancer cells. Using multiple clinically relevant model systems of human brain tumor development, such as glioblastomas in engineered cerebral organoids and patient-derived brain tumor spheroids, we translated our screen to sensitize brain cancer cells to Artemisinin using the metabolite 5-ALA, a photodynamic porphyrin enhancer. A combination treatment with Artemisinin and 5-ALA markedly killed brain tumor cells in all model systems tested, including in multidrug resistance cancers. These data uncover the critical molecular pathway for Artemisinin cytotoxicity and a sensitization strategy to treat multiple brain tumors, including largely untreatable human glioblastomas
CAR T cells targeting BCMA and CD19 for newly diagnosed and relapsed multiple myeloma patients responding to current therapy
No full textWe conducted a phase 1 clinical trial of anti-BCMA CAR T cells (CART-BCMA) with or without
anti-CD19 CAR T cells (huCART19) in multiple myeloma (MM) patients responding to third-orlater-
(Phase A, N=10) or high-risk patients responding to first-line therapy (Phase B, N=20),
followed by early lenalidomide or pomalidomide maintenance. We observed no high-grade CRS
and only one instance of low-grade neurologic toxicity. In vivo CART-BCMA expansion was
comparable to that previously observed with CART-BCMA in relapsed/refractory MM. Early
maintenance therapy was safe and feasible and coincided with CAR T cell re-expansion and
late-onset clinical response in some patients. Pre-treatment MM cell-surface BCMA levels were
unexpectedly low despite no prior exposure to anti-BCMA therapy. Despite low disease burden
and evidence of anti-MM activity in most patients, only 4 subjects converted to complete
response. Outcomes with CART-BCMA + huCART19 were similar to CART-BCMA alone
Multi-species machine learning predictions of in vitro intrinsic clearance with uncertainty quantification analyses
No full textIn pharmaceutical research, compounds are optimized for metabolic stability to avoid a too fast elimination of the drug. Intrinsic clearance (CLint) measured in liver microsomes or hepatocytes is an important parameter during lead optimization. In this work, machine learning models were developed to relate compound structure to microsomal metabolic stability and predict CLint for new compounds. A multitask (MT) learning architecture was introduced to model the CLint of six species simultaneously, giving as a result a multi-species machine learning model. MT graph neural network (MT-GNN) regression was identified as the top-performing method and an ensemble of ten MT-GNN models was evaluated prospectively. Geometric mean fold errors were consistently smaller than 2-fold. Moreover, high precision values were obtained in the prediction of ‘high’ (>300µL/min/mg) and ‘low’ (<100µL/min/mg) CLint compounds. Precision values ranged from 80% to 94% for low CLint predictions and from 75% to 97% for high CLint predictions, depending on the species. Uncertainty on experimental values and model predictions was systematically quantified. Experimental variability (aleatoric uncertainty) of all historical Novartis in vitro clearance experiments was analyzed. Interestingly, MT-GNN models’ performance approached assay’s experimental variability. Moreover, uncertainty estimation in predictions (epistemic uncertainty) enabled identifying predictions associated to lower and higher error. Taken together, our manuscript combines a multi-species deep learning model and large-scale uncertainty analyses to improve CLint predictions and facilitate early informed decisions for compound prioritization
Needle clogging of protein solutions in prefilled syringes: A two-stage process with various determinants.
No full textClogging of staked-in-needle prefilled syringes (PFS) is a sporadic and scarcely predictable event, which occurs particularly in highly concentrated protein solutions and can result in the injection of incomplete doses, especially if autoinjector devices are used for administration. A systematic screening of possible causes and triggers was performed in order to find the crucial factors of influence, the underlying mechanisms and possible measures for prevention. An essential prerequisite for the formation of a solidified clog in the needle is the ingress of liquid from the barrel, which was investigated and quantified by means of neutron imaging after storage of prefilled syringes under various conditions. The needle filling ratio increases with both the storage temperature and the storage period, as a result of atmospheric gas diffusion through the needle shield. While the air pocket in the needle is reduced by this process, diffusion of water vapor does not affect the filling ratio but instead increases the protein concentration in the needle lumen, leading to an exponential rise of the viscosity and finally to the solidification of the protein solution. Maintaining the air pocket in the needle by avoiding any diffusion promoting pressure gradient is therefore the most effective protection from clogging
Fragment-to-Lead Medicinal Chemistry Publications in 2020.
No full textFragment-based drug discovery (FBDD) continues to evolve and make an impact in the pharmaceutical sciences. We summarize successful fragment-to-lead studies that were published in 2020. Having systematically analyzed annual scientific outputs since 2015, we discuss trends and best practices in terms of fragment libraries, target proteins, screening technologies, hit-optimization strategies, and the properties of hit fragments and the leads resulting from them. As well as the tabulated Fragment-to-Lead (F2L) programs, our 2020 literature review identifies several trends and innovations that promise to further increase the success of FBDD. These include developing structurally novel screening fragments, improving fragment-screening technologies, using new computer-aided design and virtual screening approaches, and combining FBDD with other innovative drug-discovery technologies
Water: An Underestimated Solvent for Amide Bond-Forming Reactions
No full textThe construction of the amide/peptide bond is among the most performed transformations in the pharmaceutical and chemical industries. The traditional methodologies frequently rely on the use of large amounts of organic solvents, main-ly N,N-dimethylformamide (DMF), N-methyl pyrrolidone (NMP), and dichloromethane (CH2Cl2). These solvents adverse-ly impact the environment, workers' safety, and health. These adverse impacts led academia and industry toward devel-oping greener and sustainable amide forming synthetic routes to avoid, reduce or replace the use of these hazardous sol-vents. The present perspective fits into this framework and discusses the recent development of amide/peptide bond formations in water