Drug Responses in Plexiform Neurofibroma Type I (PNF1) Cell Lines Using High-Throughput Data and Combined Effectiveness and Potency.

Background: Neurofibromatosis type 1 (NF1) is a genetic disorder characterized by heterozygous germline NF1 gene mutations that predispose patients to developing plexiform neurofibromas, which are benign but often disfiguring tumors of the peripheral nerve sheath induced by loss of heterozygosity at the NF1 locus. These can progress to malignant peripheral nerve sheath tumors (MPNSTs). There are no approved drug treatments for adults with NF1-related inoperable plexiform neurofibromas, and only one drug (selumetinib), which is an FDA-approved targeted therapy for the treatment of symptomatic pediatric plexiform neurofibromas, highlighting the need for additional drug screening and development. In high-throughput screening, the effectiveness of drugs against cell lines is often assessed by measuring in vitro potency (AC50) or the area under the curve (AUC). However, the variability of dose-response curves across drugs and cell lines and the frequency of partial effectiveness suggest that these measures alone fail to provide a full picture of overall efficacy. Methods: Using concentration-response data, we combined response effectiveness (EFF) and potency (AC50) into (a) a score characterizing the effect of a compound on a single cell line, S = log[EFF/AC50], and (b) a relative score, ΔS, characterizing the relative difference between a reference (e.g., non-tumor) and test (tumor) cell line. ΔS was applied to data from high-throughput screening (HTS) of a drug panel tested on NF1-/- tumor cells, using immortalized non-tumor NF1+/- cells as a reference. Results: We identified drugs with sensitivity, targeting expected pathways, such as MAPK-ERK and PI3K-AKT, as well as serotonin-related targets, among others. The ΔS technique used here, in tandem with a supplemental ΔS web tool, simplifies HTS analysis and may provide a springboard for further investigations into drug response in NF1-related cancers. The tool may also prove useful for drug development in a variety of other cancers.

Variant calling and benchmarking in an era of complete human genome sequences.

Genetic variant calling from DNA sequencing has enabled understanding of germline variation in hundreds of thousands of humans. Sequencing technologies and variant-calling methods have advanced rapidly, routinely providing reliable variant calls in most of the human genome. We describe how advances in long reads, deep learning, de novo assembly and pangenomes have expanded access to variant calls in increasingly challenging, repetitive genomic regions, including medically relevant regions, and how new benchmark sets and benchmarking methods illuminate their strengths and limitations. Finally, we explore the possible future of more complete characterization of human genome variation in light of the recent completion of a telomere-to-telomere human genome reference assembly and human pangenomes, and we consider the innovations needed to benchmark their newly accessible repetitive regions and complex variants.

Predicting T-cell quality during manufacturing through an artificial intelligence-based integrative multiomics analytical platform.

Large-scale, reproducible manufacturing of therapeutic cells with consistently high quality is vital for translation to clinically effective and widely accessible cell therapies. However, the biological and logistical complexity of manufacturing a living product, including challenges associated with their inherent variability and uncertainties of process parameters, currently make it difficult to achieve predictable cell-product quality. Using a degradable microscaffold-based T-cell process, we developed an artificial intelligence (AI)-driven experimental-computational platform to identify a set of critical process parameters and critical quality attributes from heterogeneous, high-dimensional, time-dependent multiomics data, measurable during early stages of manufacturing and predictive of end-of-manufacturing product quality. Sequential, design-of-experiment-based studies, coupled with an agnostic machine-learning framework, were used to extract feature combinations from early in-culture media assessment that were highly predictive of the end-product CD4/CD8 ratio and total live CD4+ and CD8+ naïve and central memory T cells (CD63L+CCR7+). Our results demonstrate a broadly applicable platform tool to predict end-product quality and composition from early time point in-process measurements during therapeutic cell manufacturing.

Antioxidant potential of Juglans nigra, black walnut, husks extracted using supercritical carbon dioxide with an ethanol modifier.

The black walnut, Junglas nigra, is indigenous to eastern North America, and abscission of its fruit occurs around October. The fruit consists of a husk, a hard shell, and kernel. The husk is commonly discarded in processing, though it contains phenolic compounds that exhibit antioxidant and antimicrobial properties. For this study, black walnut husks were extracted using supercritical carbon dioxide with an ethanol modifier. The effects of temperature, ethanol concentration, and drying of walnut husks prior to extraction upon antioxidant potential were evaluated using a factorial design of experiments. The solvent density was held constant at 0.75 g/mL. The optimal extraction conditions were found to be 68°C and 20 wt-% ethanol in supercritical carbon dioxide. At these conditions, the antioxidant potential as measured by the ferric reducing ability of plasma (FRAP) assay was 0.027 mmol trolox equivalent/g (mmol TE/g) for dried walnut husk and 0.054 mmol TE/g for walnut husks that were not dried. Antioxidant potential was also evaluated using the total phenolic content (TPC) and 1,1-diphenyl-2-picryl-hydrazyl (DPPH) assays and the FRAP assay was found to linearly correlate to the TPC assay.