Optimal experimental design for efficient toxicity testing in microphysiological systems: A bone marrow application

Introduction: Microphysiological systems (MPS; organ-on-a-chip) aim to recapitulate the 3D organ microenvironment and improve clinical predictivity relative to previous approaches. Though MPS studies provide great promise to explore treatment options in a multifactorial manner, they are often very complex. It is therefore important to assess and manage technical confounding factors, to maximise power, efficiency and scalability.Methods: As an illustration of how MPS studies can benefit from a systematic evaluation of confounders, we developed an experimental design approach for a bone marrow (BM) MPS and tested it for a specified context of use, the assessment of lineage-specific toxicity.Results: We demonstrated the accuracy of our multicolour flow cytometry set-up to determine cell type and maturity, and the viability of a “repeated measures” design where we sample from chips repeatedly for increased scalability and robustness. Importantly, we demonstrated an optimal way to arrange technical confounders. Accounting for these confounders in a mixed-model analysis pipeline increased power, which meant that the expected lineage-specific toxicities following treatment with olaparib or carboplatin were detected earlier and at lower doses. Furthermore, we performed a sample size analysis to estimate the appropriate number of replicates required for different effect sizes. This experimental design-based approach will generalise to other MPS set-ups.Discussion: This design of experiments approach has established a groundwork for a reliable and reproducible in vitro analysis of BM toxicity in a MPS, and the lineage-specific toxicity data demonstrate the utility of this model for BM toxicity assessment. Toxicity data demonstrate the utility of this model for BM toxicity assessment

Transcriptional signatures associated with persisting CD19 CAR-T cells in children with leukemia

In the context of relapsed and refractory childhood pre-B cell acute lymphoblastic leukemia (R/R B-ALL), CD19-targeting chimeric antigen receptor (CAR)-T cells often induce durable remissions, which requires the persistence of CAR-T cells. In this study, we systematically analyzed CD19 CAR-T cells of 10 children with R/R B-ALL enrolled in the CARPALL trial via high-throughput single-cell gene expression and T cell receptor sequencing of infusion products and serial blood and bone marrow samples up to 5 years after infusion. We show that long-lived CAR-T cells developed a CD4/CD8 double-negative phenotype with an exhausted-like memory state and distinct transcriptional signature. This persistence signature was dominant among circulating CAR-T cells in all children with a long-lived treatment response for which sequencing data were sufficient (4/4, 100%). The signature was also present across T cell subsets and clonotypes, indicating that persisting CAR-T cells converge transcriptionally. This persistence signature was also detected in two adult patients with chronic lymphocytic leukemia with decade-long remissions who received a different CD19 CAR-T cell product. Examination of single T cell transcriptomes from a wide range of healthy and diseased tissues across children and adults indicated that the persistence signature may be specific to long-lived CAR-T cells. These findings raise the possibility that a universal transcriptional signature of clinically effective, persistent CD19 CAR-T cells exists

A phylogenomic view of ecological specialization in the Lachnospiraceae, a family of digestive tract-associated bacteria

YesSeveral bacterial families are known to be highly abundant within the human microbiome, but their ecological roles and evolutionary histories have yet to be investigated in depth. One such family, Lachnospiraceae (phylum Firmicutes, class Clostridia) is abundant in the digestive tracts of many mammals and relatively rare elsewhere. Members of this family have been linked to obesity and protection from colon cancer in humans, mainly due to the association of many species within the group with the production of butyric acid, a substance that is important for both microbial and host epithelial cell growth. We examined the genomes of 30 Lachnospiraceae isolates to better understand the origin of butyric acid capabilities and other ecological adaptations within this group. Butyric acid production-related genes were detected in fewer than half of the examined genomes with the distribution of this function likely arising in part from lateral gene transfer (LGT). An investigation of environment-specific functional signatures indicated that human gut-associated Lachnospiraceae possess genes for endospore formation, whereas other members of this family lack key sporulation-associated genes, an observation supported by analysis of metagenomes from the human gut, oral cavity, and bovine rumen. Our analysis demonstrates that adaptation to an ecological niche and acquisition of defining functional roles within a microbiome can arise through a combination of both habitat-specific gene loss and LGT.Canadian Institute for Health Research (grant number CMF-108026), Genome Atlantic and the Canada Research Chairs program to R.G.B

Molecular Dynamics Investigation of Solid State Form Prediction, Selection, and Control: Towards Application to Crystallization

Polymorphism, the ability of a molecule to self-assemble into multiple solid-state forms, intimately determines the solid-state properties of a material. In the multi-trillion dollar pharmaceutical industry, where 90% of all products are sold in the solid-state, this has tremendous implications for key tablet properties such as shelf life, bioavailability, dissolution, nucleation, and growth rates. This myriad of performance variables frequently leads to competing design interests. This is exhibited by the frequently observed case of the most stable solid-state form displaying insufficient bioavailability in vivo, requiring the manufacturing of a more bioavailable, yet less stable, polymorphic form. Furthermore, it is well known that while a particular polymorph may be more thermodynamically stable, its rate of formation (kinetics) may be slower relative to its metastable counterpart. Consequently, a pharmaceutical crystallization process that has been designed to produce only the desired stable polymorph may result in different, or worse yet, undiscovered, polymorphic forms, due to unforeseen variations in the manufacturing process conditions. These examples illustrate the importance of understanding polymorph formation and control to allow for optimal polymorphic form selection and manufacturing. Current methods of polymorphic form manufacturing and discovery, which include solution crystallization and solid-form screening, lack the appropriate level of atomic insight required to fully understand the crystallization process, require the use of costly API materials during screening, and never fully answer the question of whether all polymorphic forms have been discovered. As such, predictive simulation methodologies are desired to alleviate these issues. Although, molecular dynamics (MD), a simulation methodology which time integrates Newton’s equations of motion with fully atomistic detail, provides the necessary predictive capability, it has suffered from time scale challenges associated with activated rate processes, such as nucleation, as well as the large system sizes (\u3e100 K atoms) necessary to simulate solution crystallization, hence limiting its use for relevant API molecules. Prior to this thesis, only a handful of attempts to screen the crystallization of organic molecules have been performed. This thesis addresses the aforementioned computational and methodology limitations of MD, and demonstrates that MD can serve as an in silico screening tool for the prediction, selection, and control of polymorphic forms through atomistic insights. Hence, this work represents an essential step towards fully atomistic, in silico, design of an industrial crystallization unit. Specifically, it is demonstrated that MD can predict polymorphic form crystallization from solution, nanocrystal dissolution kinetics and solubility increase, and finally the influence of an externally applied electric field (e-field) on crystallization dynamics, crystal morphology, and polymorphic form. To make these simulations computationally accessible for the first time, a newly developed massively parallel MD engine, specifically designed for Intel® Xeon Phi™ coprocessor hardware, is used as an investigative tool. This allows simulations with fully atomistic detail and femtosecond resolution to be performed, yielding insights into the diverse physical phenomena associated with polymorphism. It is demonstrated that polymorph specific nucleation kinetics and solubility calculations allow for the correct prediction of both glycine and paracetamol polymorphs at the nanoscale and bulk (micron) length scales, in aqueous solution, through the use of the seeded cluster simulation methodology. Furthermore, nanocrystal dissolution kinetics calculations are shown to predict the fastest dissolving polymorph of glycine, and hence allow for the selection of the polymorph with highest potential bioavailability in vivo. Finally, externally applied, static, e-fields are demonstrated to be a significant control variable over nanocrystal growth and dissolution dynamics, allowing for the manipulation of particle size distributions by varying the efield intensity alone. The morphology of nanoparticles grown in the presence of the efield vectors is shown to be needle like, with principal axis either perpendicular (paracetamol) or parallel (glycine) to the applied e-field vector. Most importantly, it is demonstrated that e-fields allow for the formation of never before seen paracetamol and glycine crystal structures that maximize the alignment of solid-state molecules with the applied e-field vector. This enhanced scope for control of crystal structure with novel properties should serve the unparalleled quest for advanced materials in industries as diverse as alternative energy, pharmaceuticals, and defense

An Analysis of Proteochemometric and Conformal Prediction Machine Learning Protein-Ligand Binding Affinity Models

Protein-ligand binding affinity is a key pharmacodynamic endpoint in drug discovery. Sole reliance on experimental design, make, and test cycles is costly and time consuming, providing an opportunity for computational methods to assist. Herein, we present results comparing random forest and feed-forward neural network proteochemometric models for their ability to predict pIC50 measurements for held out generic Bemis-Murcko scaffolds. In addition, we assess the ability of conformal prediction to provide calibrated prediction intervals in both a retrospective and semi-prospective test using the recently released Grand Challenge 4 data set as an external test set. In total, random forest and deep neural network proteochemometric models show quality retrospective performance but suffer in the semi-prospective setting. However, the conformal predictor prediction intervals prove to be well calibrated both retrospectively and semi-prospectively showing that they can be used to guide hit discovery and lead optimization campaigns.</div

Machine Learning for Acute Oral System Toxicity Regression and Classification

In vivotoxicity testing remains a costly and time-consuming component of any pre-clinical drug development campaign. In particular, LD50 measurements require the loss of animal life but remain a critical component in preventing lethal compounds from entering the clinic. With advances in machine learning, in silicoLD50 prediction now has the potential to greatly reduce this burden. We study various types of machine learning models to predict acute oral LD50 measurements in rats as regression and classification problems. We demonstrate that transfer learning a ResNet34 model pretrained on ImageNet with test time augmentation generates the best performing regression model and that random forest augmented with conformal prediction provides a robust methodology to perform classification.</p

Evaluation of Binding Site Comparison Algorithms and Proteometric Machine Learning Models in the Detection of Protein Pockets Capable of Binding the Same Ligand

Non linearities of biological networks present ample opportunity for synergistic protein targeting combinations. Yet, to date, our ability to design multi-target inhibitors and predict polypharmacology binding profiles remains limited. Herein, we present a systematic benchmarking of protein pocket comparison algorithms from the literature, as well as novel machine learning models developed to predict whether two proteins will bind the same ligand. The results demonstrate that previously reported performance metrics from the literature could be inflated due to a bias towards proteins of similar folds when identifying protein capable of binding the same ligand. This observation motivated a more in-depth evaluation of the methods against two subsets of same and cross protein fold comparisons. In a head to head comparison using the cross protein fold subset, we found that the proteometric machine learning models were the best performing models overall. </div

Exploring New Crystal Structures of Glycine via Electric Field-Induced Structural Transformations with Molecular Dynamics Simulations

Being able to control polymorphism of a crystal is of great importance to many industries, including the pharmaceutical industry, since the crystal&#8217;s structure determines significant physical properties of a material. While there are many conventional methods used to control the final crystal structure that comes out of a crystallization unit, these methods fail to go beyond a few known structures that are kinetically accessible. Recent studies have shown that externally applied fields have the potential to effectively control polymorphism and to extend the set of observable polymorphs that are not accessible through conventional methods. This computational study focuses on the application of high-intensity dc electric fields (e-fields) to induce solid-state transformation of glycine crystals to obtain new polymorphs that have not been observed via experiments. Through molecular dynamics simulations of solid-state &#945; -, &#946; -, and &#947; -glycine crystals, it has been shown that the new polymorphs sustain their structures within 125 ns after the electric field has been turned off. It was also demonstrated that strength and direction of the electric field and the initial structure of the crystal are parameters that affect the resulting polymorph. Our results showed that application of high-intensity dc electric fields on solid-state crystals can be an effective crystal structure control method for the exploration of new crystal structures of known materials and to extend the range of physical properties a material can have