•Few studies characterize combined immunotherapy and radiation.•Toxicities similar after adding radiation to immunotherapy.•Anti-CTLA-4 associated with more toxicity than anti-PD-1/PD-L1.•No ...significant difference based on timing of immunotherapy and radiation.
Immune checkpoint inhibitor with radiation therapy (ICI + RT) is under investigation for improved patient outcome, so we performed a systematic review/meta-analysis of toxicities for ICI + RT compared to immune checkpoint inhibitor (ICI) therapy alone.
A PRISMA-compliant systematic review of studies in MEDLINE (PubMed) and in the National Comprehensive Cancer Network guidelines was conducted, with primary outcome grade 3 + toxicity. Criteria for ICI alone were: phase III/IV trials that compared immunotherapy to placebo, chemotherapy, or alternative immunotherapy; and for ICI + RT: prospective/retrospective studies with an arm treated with ICI + RT. Meta-analysis was performed by random effects models using the DerSimonian and Laird method. The I2 statistic and Cochran’s Q test were used to assess heterogeneity, while funnel plots and Egger’s test assessed publication bias.
This meta-analysis included 51 studies (n = 15,398), with 35 ICI alone (n = 13,956) and 16 ICI + RT studies (n = 1,442). Our models showed comparable grade 3–4 toxicities in ICI + RT (16.3%; 95% CI, 11.1–22.3%) and ICI alone (22.3%; 95% CI, 18.1–26.9%). Stratification by timing of radiation and irradiated site showed no significant differences, but anti-CTLA-4 therapy and melanoma showed increased toxicity. The grade 5 toxicities were 1.1% and 1.9% for ICI alone and ICI + RT respectively. There was significant heterogeneity, but not publication bias.
The random effects model showed comparable grade 3–4 toxicity in using ICI + RT compared to ICI alone in CNS melanoma metastases, NSCLC, and prostate cancer. ICI + RT is safe for future clinical trials in these cancers.
Fast and accurate 3D RNA structure prediction remains a major challenge in structural biology, mostly due to the size and flexibility of RNA molecules, as well as the lack of diverse experimentally ...determined structures of RNA molecules. Unlike DNA structure, RNA structure is far less constrained by basepair hydrogen bonding, resulting in an explosion of potential stable states. Here, we propose a convolutional neural network that predicts all pairwise distances between residues in an RNA, using a recently described smooth parametrization of Euclidean distance matrices. We achieve high-accuracy predictions on RNAs up to 100 nt in length in fractions of a second, a factor of 107 faster than existing molecular dynamics-based methods. We also convert our coarse-grained machine learning output into an all-atom model using discrete molecular dynamics with constraints. Our proposed computational pipeline predicts all-atom RNA models solely from the nucleotide sequence. However, this method suffers from the same limitation as nucleic acid molecular dynamics: the scarcity of available RNA crystal structures for training.
Molecular dynamics (MD) is the primary computational method by which modern structural biology explores macromolecule structure and function. Boltzmann generators have been proposed as an alternative ...to MD, by replacing the integration of molecular systems over time with the training of generative neural networks. This neural network approach to MD enables convergence to thermodynamic equilibrium faster than traditional MD; however, critical gaps in the theory and computational feasibility of Boltzmann generators significantly reduce their usability. Here, we develop a mathematical foundation to overcome these barriers; we demonstrate that the Boltzmann generator approach is sufficiently rapid to replace traditional MD for complex macromolecules, such as proteins in specific applications, and we provide a comprehensive toolkit for the exploration of molecular energy landscapes with neural networks.
Precise RNA tertiary structure prediction can aid in the design of RNA nanoparticles. However, most existing RNA tertiary structure prediction methods are limited to small RNAs with relatively simple ...secondary structures. Large RNA molecules usually have complex secondary structures, including multibranched loops and pseudoknots, allowing for highly flexible RNA geometries and multiple stable states. Various experiments and bioinformatics analyses can often provide information about the distance between atoms (or residues) in RNA, which can be used to guide the prediction of RNA tertiary structure. In this chapter, we will introduce a platform, iFoldNMR, that can incorporate non-exchangeable imino protons resonance data from NMR as restraints for RNA 3D structure prediction. We also introduce an algorithm, DVASS, which optimizes distance restraints for better RNA 3D structure prediction.
Protein aggregation results in an array of different size soluble oligomers and larger insoluble fibrils. Insoluble fibrils were originally thought to cause neuronal cell deaths in neurodegenerative ...diseases due to their prevalence in tissue samples and disease models. Despite recent studies demonstrating the toxicity associated with soluble oligomers, many therapeutic strategies still focus on fibrils or consider all types of aggregates as one group. Oligomers and fibrils require different modeling and therapeutic strategies, targeting the toxic species is crucial for successful study and therapeutic development. Here, we review the role of different‐size aggregates in disease, and how factors contributing to aggregation (mutations, metals, post‐translational modifications, and lipid interactions) may promote oligomers opposed to fibrils. We review two different computational modeling strategies (molecular dynamics and kinetic modeling) and how they are used to model both oligomers and fibrils. Finally, we outline the current therapeutic strategies targeting aggregating proteins and their strengths and weaknesses for targeting oligomers versus fibrils. Altogether, we aim to highlight the importance of distinguishing the difference between oligomers and fibrils and determining which species is toxic when modeling and creating therapeutics for protein aggregation in disease.
A series of compounds were designed utilizing molecular modeling and fragment-based design based upon the known protein phosphatase 2A (PP2A) activators, NSC49L and iHAP1, and evaluated for their ...ability to inhibit the viability of colorectal cancer (CRC) and folinic acid, 5-fluorouracil, and oxaliplatin (FOLFOX)-resistant CRC cells. PPA24 (19a) was identified as the most cytotoxic compound with IC50 values in the range of 2.36-6.75 μM in CRC and FOLFOX-resistant CRC cell lines. It stimulated PP2A activity to a greater extent, displayed lower binding energies through molecular docking, and showed higher binding affinity through surface plasmon resonance for PP2A catalytic subunit α than the known PP2A activators. PPA24 dose-dependently induced apoptosis and oxidative stress, decreased the level of c-Myc expression, and synergistically potentiated cytotoxicity when combined with gemcitabine and cisplatin. Furthermore, a PPA24-encapsulated nanoformulation significantly inhibited the growth of CRC xenografts without systemic toxicities. Together, these results signify the potential of PPA24 as a novel PP2A activator and a prospective therapeutic for CRC and FOLFOX-resistant CRC.A series of compounds were designed utilizing molecular modeling and fragment-based design based upon the known protein phosphatase 2A (PP2A) activators, NSC49L and iHAP1, and evaluated for their ability to inhibit the viability of colorectal cancer (CRC) and folinic acid, 5-fluorouracil, and oxaliplatin (FOLFOX)-resistant CRC cells. PPA24 (19a) was identified as the most cytotoxic compound with IC50 values in the range of 2.36-6.75 μM in CRC and FOLFOX-resistant CRC cell lines. It stimulated PP2A activity to a greater extent, displayed lower binding energies through molecular docking, and showed higher binding affinity through surface plasmon resonance for PP2A catalytic subunit α than the known PP2A activators. PPA24 dose-dependently induced apoptosis and oxidative stress, decreased the level of c-Myc expression, and synergistically potentiated cytotoxicity when combined with gemcitabine and cisplatin. Furthermore, a PPA24-encapsulated nanoformulation significantly inhibited the growth of CRC xenografts without systemic toxicities. Together, these results signify the potential of PPA24 as a novel PP2A activator and a prospective therapeutic for CRC and FOLFOX-resistant CRC.
The stress response protein regulated in development and DNA damage response 1 (REDD1) has been implicated in visual deficits in patients with diabetes. The aim here was to investigate the mechanism ...responsible for the increase in retinal REDD1 protein content that is observed with diabetes. We found that REDD1 protein expression was increased in the retina of streptozotocin-induced diabetic mice in the absence of a change in REDD1 mRNA abundance or ribosome association. Oral antioxidant supplementation reduced retinal oxidative stress and suppressed REDD1 protein expression in the retina of diabetic mice. In human retinal Müller cell cultures, hyperglycemic conditions increased oxidative stress, enhanced REDD1 expression, and inhibited REDD1 degradation independently of the proteasome. Hyperglycemic conditions promoted a redox-sensitive cross-strand disulfide bond in REDD1 at C150/C157 that was required for reduced REDD1 degradation. Discrete molecular dynamics simulations of REDD1 structure revealed allosteric regulation of a degron upon formation of the disulfide bond that disrupted lysosomal proteolysis of REDD1. REDD1 acetylation at K129 was required for REDD1 recognition by the cytosolic chaperone HSC70 and degradation by chaperone-mediated autophagy. Disruption of REDD1 allostery upon C150/C157 disulfide bond formation prevented the suppressive effect of hyperglycemic conditions on REDD1 degradation and reduced oxidative stress in cells exposed to hyperglycemic conditions. The results reveal redox regulation of REDD1 and demonstrate the role of a REDD1 disulfide switch in development of oxidative stress.
The abscopal effect is defined when a form of local therapy causes tumor regression of both the target lesion and any untreated tumors. Herein cases of the abscopal effect were systematically ...reviewed and a patient-level data analysis was performed for clinical predictors of both duration of response and survival.
The Population, Intervention, Control, Outcome, Study (PICOS) design approach, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) literature selection process, and Meta-analysis of Observational Studies in Epidemiology (MOOSE) were used to find articles published before September 2019 in MEDLINE/PubMed and Google Scholar. Inclusion criteria were (1) population: patients with reported abscopal response; (2) intervention: documented treatment(s); (3) control: none; (4) outcomes: overall and progression-free survival; and (5) setting: retrospective case reports. Time from treatment until abscopal response and time from abscopal response until progression/death were calculated. Univariate and multivariate analyses were conducted for survival outcomes.
Fifty studies (n = 55 patients) were included. Median age was 65 years (interquartile range IQR, 58-70) and 62% were male. Fifty-four (98%) patients received radiation therapy, 34 (62%) received radiation therapy alone, 5 (9.1%) underwent surgery, 4 (7.3%) received chemotherapy, and 11 (20%) received immunotherapy. Median total dose was 32 Gy (IQR, 25.5-48 Gy) and median dose per fraction was 3 Gy (IQR, 2-7.2). Median time until abscopal response was 4 months (IQR, 1-5; min 0.5, max 24). At 5 years, overall survival was 63% and distant progression-free survival was 45%. No variables had statistical significance in predicting duration of response or survival.
Almost all reported cases of the abscopal response are after radiation therapy; however, there are no known predictors of duration of response or survival in this population.
Virtual screening is a cost- and time-effective alternative to traditional high-throughput screening in the drug discovery process. Both virtual screening approaches, structure-based molecular ...docking and ligand-based cheminformatics, suffer from computational cost, low accuracy, and/or reliance on prior knowledge of a ligand that binds to a given target. Here, we propose a neural network framework, NeuralDock, which accelerates the process of high-quality computational docking by a factor of 10
, and does not require prior knowledge of a ligand that binds to a given target. By approximating both protein-small molecule conformational sampling and energy-based scoring, NeuralDock accurately predicts the binding energy, and affinity of a protein-small molecule pair, based on protein pocket 3D structure and small molecule topology. We use NeuralDock and 25 GPUs to dock 937 million molecules from the ZINC database against superoxide dismutase-1 in 21 h, which we validate with physical docking using MedusaDock. Due to its speed and accuracy, NeuralDock may be useful in brute-force virtual screening of massive chemical libraries and training of generative drug models.
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