Computational repurposing of oncology drugs through off-target drug binding interactions from pharmacological databases.

PURPOSE: Systematic repurposing of approved medicines for another indication may accelerate drug development in oncology. We present a strategy combining biomarker testing with drug repurposing to identify new treatments for patients with advanced cancer. METHODS: Tumours were sequenced with the Illumina TruSight Oncology 500 (TSO-500) platform or the FoundationOne CDx panel. Mutations were screened by two medical oncologists and pathogenic mutations were categorised referencing literature. Variants of unknown significance were classified as potentially pathogenic using plausible mechanisms and computational prediction of pathogenicity. Gain of function (GOF) mutations were evaluated through repurposing databases Probe Miner (PM), Broad Institute Drug Repurposing Hub (Broad Institute DRH) and TOPOGRAPH. GOF mutations were repurposing events if identified in PM, not indexed in TOPOGRAPH and excluding mutations with a known Food and Drug Administration (FDA)-approved biomarker. The computational repurposing approach was validated by evaluating its ability to identify FDA-approved biomarkers. The total repurposable genome was identified by evaluating all possible gene-FDA drug-approved combinations in the PM dataset. RESULTS: The computational repurposing approach was accurate at identifying FDA therapies with known biomarkers (94%). Using next-generation sequencing molecular reports (n = 94), a meaningful percentage of patients (14%) could have an off-label therapeutic identified. The frequency of theoretical drug repurposing events in The Cancer Genome Atlas pan-cancer dataset was 73% of the samples in the cohort. CONCLUSION: A computational drug repurposing approach may assist in identifying novel repurposing events in cancer patients with no access to standard therapies. Further validation is needed to confirm a precision oncology approach using drug repurposing.

The associative ionization of N(2P) + O(3P).

The rate constant of the associative ionization reaction N(2P) + O(3P) → NO+ + e- was measured using a flow tube apparatus. A flowing afterglow source was used to produce an ion/electron plasma containing a mixture of ions, including N2+, N3+, and N4+. Dissociative recombination of these species produced a population of nitrogen atoms, including N(2P). Charged species were rejected from the flow tube using an electrostatic grid, subsequent to which oxygen atoms were introduced, produced either using a discharge of helium and oxygen or via the titration of nitrogen atoms with NO. Only the title reaction can produce the NO+ observed after the introduction of O atoms. The resulting rate constant (8 ± 5 ×10-11 cm3 s-1) is larger than previously reported N(2P) + O disappearance rate constants (∼2 × 10-11 cm3 s-1). The possible errors in this or previous experiments are discussed. It is concluded that the N(2P) + O(3P) reaction proceeds almost entirely by associative ionization, with quenching to the 2D or 4S states as only minor processes.

Cheminformatics-Guided Cell-Free Exploration of Peptide Natural Products.

There have been significant advances in the flexibility and power of in vitro cell-free translation systems. The increasing ability to incorporate noncanonical amino acids and complement translation with recombinant enzymes has enabled cell-free production of peptide-based natural products (NPs) and NP-like molecules. We anticipate that many more such compounds and analogs might be accessed in this way. To assess the peptide NP space that is directly accessible to current cell-free technologies, we developed a peptide parsing algorithm that breaks down peptide NPs into building blocks based on ribosomal translation logic. Using the resultant data set, we broadly analyze the biophysical properties of these privileged compounds and perform a retrobiosynthetic analysis to predict which peptide NPs could be directly synthesized in augmented cell-free translation reactions. We then tested these predictions by preparing a library of highly modified peptide NPs. Two macrocyclases, PatG and PCY1, were used to effect the head-to-tail macrocyclization of candidate NPs. This retrobiosynthetic analysis identified a collection of high-priority building blocks that are enriched throughout peptide NPs, yet they had not previously been tested in cell-free translation. To expand the cell-free toolbox into this space, we established, optimized, and characterized the flexizyme-enabled ribosomal incorporation of piperazic acids. Overall, these results demonstrate the feasibility of cell-free translation for peptide NP total synthesis while expanding the limits of the technology. This work provides a novel computational tool for exploration of peptide NP chemical space, that could be expanded in the future to allow design of ribosomal biosynthetic pathways for NPs and NP-like molecules.

Analytical Framework to Understand the Origins of Methyl Side-Chain Dynamics in Protein Assemblies.

Side-chain motions play an important role in understanding protein structure, dynamics, protein-protein, and protein-ligand interactions. However, our understanding of protein side-chain dynamics is currently limited by the lack of analytical tools. Here, we present a novel analytical framework employing experimental nuclear magnetic resonance (NMR) relaxation measurements at atomic resolution combined with molecular dynamics (MD) simulation to characterize with a high level of detail the methyl side-chain dynamics in insoluble protein assemblies, using amyloid fibrils formed by the prion HET-s. We use MD simulation to interpret experimental results, where rotameric hops, including methyl group rotation and χ1/χ2 rotations, cannot be completely described with a single correlation time but rather sample a broad distribution of correlation times, resulting from continuously changing local structure in the fibril. Backbone motion similarly samples a broad range of correlation times, from ∼100 ps to µs, although resulting from mostly different dynamic processes; nonetheless, we find that the backbone is not fully decoupled from the side-chain motion, where changes in side-chain dynamics influence backbone motion and vice versa. While the complexity of side-chain motion in protein assemblies makes it very challenging to obtain perfect agreement between experiment and simulation, our analytical framework improves the interpretation of experimental dynamics measurements for complex protein assemblies.

Novel SGCE Mutation in a Patient With Myoclonus-Dystonia: A Case Report.

Objectives: Characterize the presentation, workup, and management of SGCE myoclonus-dystonia, a rare genetic condition, in a patient with atypical presenting symptoms and no family history of movement abnormalities. Methods: A woman with myoclonus and dystonia was identified based on clinical history and physical examination. Workup was conducted to determine the cause of her symptoms, including whole-exome sequencing. Myoclonus-dystonia is associated with more than 100 distinct mutations in MYC/DYT-SGCE that account for only half of the total myoclonus-dystonia patients. As such, this case required intensive genetic analyses rather than screening only for a small subset of well-characterized mutations. Results: Childhood onset myoclonus and worsening dystonia with age were identified in a young woman. She underwent screening for common causes of twitching movements, followed by whole-exome sequencing which identified a de novo novel variant in the SGCE gene, resulting in a diagnosis of SGCE myoclonus-dystonia. Discussion: Myoclonus-dystonia should be considered in patients with symptoms of head and upper extremity myoclonus early in life, especially with co-occurring dystonia, even in the absence of a family history of similar symptoms. Diagnosis of this condition should take place using sequencing, as new mutations continue to be discovered.

HTRA1 disaggregates α-synuclein amyloid fibrils and converts them into non-toxic and seeding incompetent species.

Parkinson's disease (PD) is closely linked to α-synuclein (α-syn) misfolding and accumulation in Lewy bodies. The PDZ serine protease HTRA1 degrades fibrillar tau, which is associated with Alzheimer's disease, and inactivating mutations to mitochondrial HTRA2 are implicated in PD. Here, we report that HTRA1 inhibits aggregation of α-syn as well as FUS and TDP-43, which are implicated in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. The protease domain of HTRA1 is necessary and sufficient for inhibiting aggregation, yet this activity is proteolytically-independent. Further, HTRA1 disaggregates preformed α-syn fibrils, rendering them incapable of seeding aggregation of endogenous α-syn, while reducing HTRA1 expression promotes α-syn seeding. HTRA1 remodels α-syn fibrils by targeting the NAC domain, the key domain catalyzing α-syn amyloidogenesis. Finally, HTRA1 detoxifies α-syn fibrils and prevents formation of hyperphosphorylated α-syn accumulations in primary neurons. Our findings suggest that HTRA1 may be a therapeutic target for a range of neurodegenerative disorders.

How many microplastics do you need to (sub)sample?

Analysis of microplastics in the environment requires polymer characterization as a confirmation step for suspected microplastic particles found in a sample. Material characterization is costly and can take a long time per particle. When microplastic particle counts are high, many researchers cannot characterize every particle in their sample due to time or monetary constraints. Moreover, characterizing every particle in samples with high plastic particle counts is unnecessary for describing the sample properties. We propose an a priori approach to determine the number of suspected microplastic particles in a sample that should be randomly subsampled for characterization to accurately assess the polymer distribution in the environmental sample. The proposed equation is well-founded in statistics literature and was validated using published microplastic data and simulations for typical microplastic subsampling routines. We report values from the whole equation but also derive a simple way to calculate the necessary particle count for samples or subsamples by taking the error to the power of negative two. Assuming an error of 0.05 (5 %) with a confidence interval of 95 %, an unknown expected proportion, and a sample with many particles (> 100k), the minimum number of particles in a subsample should be 386 particles to accurately characterize the polymer distribution of the sample, given the particles are randomly characterized from the full population of suspected particles. Extending this equation to simultaneously estimate polymer, color, size, and morphology distributions reveals more particles (620) would be needed in the subsample to achieve the same high absolute error threshold for all properties. The above proposal for minimum subsample size also applies to the minimum count that should be present in samples to accurately characterize particle type presence and diversity in a given environmental compartment.

Factors affecting pharmacology learning in integrated PBL in diverse medical students: a mixed methods study.

INTRODUCTION: Problem-based learning (PBL) was introduced to address passive teaching limitations. However, it is not fully characterised as a teaching modality in pharmacology. The present study investigated the factors affecting pharmacology learning in an integrated PBL-based curriculum in diverse learners. METHODS: Year 1 undergraduate medical students from two cohorts at St. George's University of London and University of Nicosia, participated. Statistical analysis of pharmacology knowledge scores, at the beginning (pre-test) and end of the academic year (post-test), investigated readiness to benefit from PBL based on diverse student characteristics (educational background, age, gender, country of origin, ethnicity, native language, PBL experience). Focus groups/interviews and a survey investigated aspects of integrated PBL impacting learning in depth. RESULTS: Pre- and post-test scores were positively correlated. Students with biomedical sciences degrees performed better at the pharmacology pre- and post-tests, while post-graduate degree holders performed better only at the pre-test. Effect size was of moderate magnitude. However, progress in learning (post-test performance after controlling for pre-test scores) was unaffected. Qualitative analysis revealed three major themes: 1) PBL as a learning environment; 2) PBL as a learning environment in pharmacology; and 3) PBL as a learning environment and confidence in prescribing. Under theme one, skill development, knowledge acquisition through collaboration and self-directed learning, group dynamics and preferred teaching methods were discussed. Under theme two, contextual learning, depth of knowledge and material correctness were raised. Under theme 3, students expressed variability in prescribing confidence. They perceived that learning could be improved by better integration, further references earlier on, more lectures and PBL facilitators with greater content expertise. The survey findings were consistent with those from focus groups/interviews. CONCLUSION: Pharmacology learning in a PBL-based curriculum is facilitated by constructive, collaborative and contextual learning. While baseline pharmacology knowledge may be advantageous, the other aforementioned characteristics studied may not affect readiness to benefit from PBL. However, further instructional scaffolding is needed, for example through further resources, lectures and self-assessment. The results from our study can inform evidence-based curriculum reform to support student learning further. Addressing learning needs could ultimately contribute to reducing medication errors through effective training of future prescribers.

Longitudinal prognosis of Parkinson's outcomes using causal connectivity.

Despite the prevalence of Parkinson's disease (PD), there are no clinically-accepted neuroimaging biomarkers to predict the trajectory of motor or cognitive decline or differentiate Parkinson's disease from atypical progressive parkinsonian diseases. Since abnormal connectivity in the motor circuit and basal ganglia have been previously shown as early markers of neurodegeneration, we hypothesize that patterns of interregional connectivity could be useful to form patient-specific predictive models of disease state and of PD progression. We use fMRI data from subjects with Multiple System Atrophy (MSA), Progressive Supranuclear Palsy (PSP), idiopathic PD, and healthy controls to construct predictive models for motor and cognitive decline and differentiate between the four subgroups. Further, we identify the specific connections most informative for progression and diagnosis. When predicting the one-year progression in the MDS-UPDRS-III1* and Montreal Cognitive assessment (MoCA), we achieve new state-of-the-art mean absolute error performance. Additionally, the balanced accuracy we achieve in the diagnosis of PD, MSA, PSP, versus healthy controls surpasses that attained in most clinics, underscoring the relevance of the brain connectivity features. Our models reveal the connectivity between deep nuclei, motor regions, and the thalamus as the most important for prediction. Collectively these results demonstrate the potential of fMRI connectivity as a prognostic biomarker for PD and increase our understanding of this disease.

Nano- and microplastic PBK modeling in the context of human exposure and risk assessment.

Insufficient data on nano- and microplastics (NMP) hinder robust evaluation of their potential health risks. Methodological disparities and the absence of established toxicity thresholds impede the comparability and practical application of research findings. The diverse attributes of NMP, such as variations in sizes, shapes, and compositions, complicate human health risk assessment. Although probability density functions (PDFs) show promise in capturing this diversity, their integration into risk assessment frameworks is limited. Physiologically based kinetic (PBK) models offer a potential solution to bridge the gap between external exposure and internal dosimetry for risk evaluation. However, the heterogeneity of NMP poses challenges for accurate biodistribution modeling. A literature review, encompassing both experimental and modeling studies, was conducted to examine biodistribution studies of monodisperse micro- and nanoparticles. The literature search in PubMed and Scopus databases yielded 39 studies that met the inclusion criteria. Evaluation criteria were adapted from previous Quality Assurance and Quality Control (QA-QC) studies, best practice guidelines from WHO (2010), OECD guidance (2021), and additional criteria specific to NMP risk assessment. Subsequently, a conceptual framework for a comprehensive NMP-PBK model was developed, addressing the multidimensionality of NMP particles. Parameters for an NMP-PBK model are presented. QA-QC evaluations revealed that most experimental studies scored relatively well (>0) in particle characterizations and environmental settings but fell short in criteria application for biodistribution modeling. The evaluation of modeling studies revealed that information regarding the model type and allometric scaling requires improvement. Three potential applications of PDFs in PBK modeling of NMP are identified: capturing the multidimensionality of the NMP continuum, quantifying the probabilistic definition of external exposure, and calculating the bio-accessibility fraction of NMP in the human body. A framework for an NMP-PBK model is proposed, integrating PDFs to enhance the assessment of NMP's impact on human health.

Development of a novel prostate Cancer-Stroma Sphere (CSS) model for In Vitro tumor microenvironment studies.

Tumor employs non-cancerous cells to gain beneficial features that promote growth and survival of cancer cells. Despite intensive research in the area of tumor microenvironment, there is still a lack of reliable and reproducible in vitro model for tumor and tumor-microenvironment cell interaction studies. Herein we report the successful development of a heterogeneous cancer-stroma sphere (CSS) model composed of prostate adenocarcinoma PC3 cells and immortalized mesenchymal stem cells (MSC). The CSS model demonstrated a structured spatial layout of the cells, with stromal cells concentrated at the center of the spheres and tumor cells located on the periphery. Significant increase in the levels of VEGFA, IL-10, and IL1a has been detected in the conditioned media of CSS as compared to PC3 spheres. Single cell RNA sequencing data revealed that VEGFA was secreted by MSC cells within heterogeneous spheroids. Enhanced expression of extracellular membrane (ECM) proteins was also shown for CSS-derived MSCs. Furthermore, we demonstrated that the multicellular architecture altered cancer cell response to chemotherapeutic agents: the inhibition of sphere formation by topotecan was 74.92 ± 4.56 % for PC3 spheres and 45.95 ± 7.84 % for CSS spheres (p < 0.01), docetaxel showed 37,51± 20,88 % and 15,67± 14,08 % inhibition, respectively (p < 0.05). Thus, CSS present an effective in vitro model for examining the extracellular matrix composition and cell-to-cell interactions within the tumor, as well as for evaluating the antitumor activity of drugs.

Unraveling motion in proteins by combining NMR relaxometry and molecular dynamics simulations: A case study on ubiquitin.

Nuclear magnetic resonance (NMR) relaxation experiments shine light onto the dynamics of molecular systems in the picosecond to millisecond timescales. As these methods cannot provide an atomically resolved view of the motion of atoms, functional groups, or domains giving rise to such signals, relaxation techniques have been combined with molecular dynamics (MD) simulations to obtain mechanistic descriptions and gain insights into the functional role of side chain or domain motion. In this work, we present a comparison of five computational methods that permit the joint analysis of MD simulations and NMR relaxation experiments. We discuss their relative strengths and areas of applicability and demonstrate how they may be utilized to interpret the dynamics in MD simulations with the small protein ubiquitin as a test system. We focus on the aliphatic side chains given the rigidity of the backbone of this protein. We find encouraging agreement between experiment, Markov state models built in the χ1/χ2 rotamer space of isoleucine residues, explicit rotamer jump models, and a decomposition of the motion using ROMANCE. These methods allow us to ascribe the dynamics to specific rotamer jumps. Simulations with eight different combinations of force field and water model highlight how the different metrics may be employed to pinpoint force field deficiencies. Furthermore, the presented comparison offers a perspective on the utility of NMR relaxation to serve as validation data for the prediction of kinetics by state-of-the-art biomolecular force fields.

Immunology of SARS-CoV-2 Infection.

According to the data from the World Health Organization, about 800 million of the world population had contracted coronavirus infection caused by SARS-CoV-2 by mid-2023. Properties of this virus have allowed it to circulate in the human population for a long time, evolving defense mechanisms against the host immune system. Severity of the disease depends largely on the degree of activation of the systemic immune response, including overstimulation of macrophages and monocytes, cytokine production, and triggering of adaptive T- and B-cell responses, while SARS-CoV-2 evades the immune system actions. In this review, we discuss immune responses triggered in response to the SARS-CoV-2 virus entry into the cell and malfunctions of the immune system that lead to the development of severe disease.

Safety and Diagnostic Efficacy of Image-Guided Biopsy of Small Renal Masses.

INTRODUCTION: Image-guided renal mass biopsy is gaining increased diagnostic acceptance, but there are limited data concerning the safety and diagnostic yield of biopsy for small renal masses (≤4 cm). This study evaluated the safety, diagnostic yield, and management after image-guided percutaneous biopsy for small renal masses. METHODS: A retrospective IRB-approved study was conducted on patients who underwent renal mass biopsy for histopathologic diagnosis at a single center from 2015 to 2021. Patients with a prior history of malignancy or a renal mass >4 cm were excluded. Descriptive statistics were used to summarize patient demographics, tumor size, the imaging modality used for biopsy, procedure details, complications, pathological diagnosis, and post-biopsy management. A biopsy was considered successful when the specimen was sufficient for diagnosis without need for a repeat biopsy. Complications were graded according to the SIR classification of adverse events. A chi-squared test (significance level set at p ≤ 0.05) was used to compare the success rate of biopsies in different lesion size groups. RESULTS: A total of 167 patients met the inclusion criteria. The median age was 65 years (range: 26-87) and 51% were male. The median renal mass size was 2.6 cm (range: one-four). Ultrasound was solely employed in 60% of procedures, CT in 33%, a combination of US/CT in 6%, and MRI in one case. With on-site cytopathology, the median number of specimens obtained per procedure was four (range: one-nine). The overall complication rate was 5%. Grade A complications were seen in 4% (n = 7), consisting of perinephric hematoma (n = 6) and retroperitoneal hematoma (n = 1). There was one grade B complication (0.5%; pain) and one grade D complication (0.5%; pyelonephritis). There was no patient mortality within 30 days post-biopsy. Biopsy was successful in 88% of cases. A sub-group analysis showed a success rate of 85% in tumors <3 cm and 93% in tumors ≥3 cm (p = 0.01). Pathological diagnoses included renal cell carcinoma (65%), oncocytoma (18%), clear cell papillary renal cell tumors (9%), angiomyolipoma (4%), xanthogranulomatous pyelonephritis (1%), lymphoma (1%), high-grade papillary urothelial carcinoma (1%), and metanephric adenoma (1%), revealing benign diagnosis in 30% of cases. The most common treatment was surgery (40%), followed by percutaneous cryoablation (22%). In total, 37% of patients were managed conservatively, and one patient received chemotherapy. CONCLUSION: This study demonstrates the safety and diagnostic efficacy of image-guided biopsy of small renal masses. The diagnostic yield was significantly higher for masses 3-4 cm in size compared to those <3 cm. The biopsy results showed a high percentage of benign diagnoses and informed treatment decisions in most patients.

Source-specific probabilistic risk assessment of microplastics in soils applying quality criteria and data alignment methods.

The risk characterization of microplastics (MP) in soil is challenging due to the non-alignment of existing exposure and effect data. Therefore, we applied data alignment methods to assess the risks of MP in soils subject to different sources of MP pollution. Our findings reveal variations in MP characteristics among sources, emphasizing the need for source-specific alignments. To assess the reliability of the data, we applied Quality Assurance/Quality Control (QA/QC) screening tools. Risk assessment was carried out probabilistically, considering uncertainties in data alignments and effect thresholds. The Hazardous Concentrations for 5% (HC5) of the species were significantly higher compared to earlier studies and ranged between 4.0 × 107 and 2.3 × 108 particles (1-5000 µm)/kg of dry soil for different MP sources and ecologically relevant metrics. The highest risk was calculated for soils with MP entering via diffuse and unspecified local sources, i.e., "background pollution". However, the source with the highest proportion of high-risk values was sewage, followed by background pollution and mulching. Notably, locations exceeding the risk threshold obtained low scores in the QA/QC assessment. No risks were observed for soils with compost. To improve future risk assessments, we advise to primarily test environmentally relevant MP mixtures and adhere to strict quality criteria.

Cancer of unknown primary and the «seed and soil¼ hypothesis.

The worldwide incidence rate of cancer of unknown primary (CUP) reaches 5% (Kang et al, 2021; Lee, Sanoff, 2020; Yang et al, 2022). CUP has an alarmingly high mortality rate, with 84% of patients succumbing within the first year following diagnosis (Registration and Service, 2018). Under normal circumstances, tumor cell metastasis follows the «seed and soil¼ hypothesis, displaying a tissue-specific pattern of cancer cell homing behavior based on the microenvironment composition of secondary organs. In this study, we questioned whether seed and soil concept applies to CUP, and whether the pattern of tumor and metastasis manifestations for cancer of known primary (CKP) can be used to inform diagnostic strategies for CUP. We compared data from metastatic and primary CUP foci to the metastasis patterns observed in CKP. Furthermore, we evaluated several techniques for identifying the tissue-of-origin (TOO) in CUP profiling, including DNA, RNA, and epigenetic TOO techniques.

Identification of differential biological activity and synergy between the PARP inhibitor rucaparib and its major metabolite.

The (poly)pharmacology of drug metabolites is seldom comprehensively characterized in drug discovery. However, some drug metabolites can reach high plasma concentrations and display in vivo activity. Here, we use computational and experimental methods to comprehensively characterize the kinase polypharmacology of M324, the major metabolite of the PARP1 inhibitor rucaparib. We demonstrate that M324 displays unique PLK2 inhibition at clinical concentrations. This kinase activity could have implications for the efficacy and safety of rucaparib and therefore warrants further clinical investigation. Importantly, we identify synergy between the drug and the metabolite in prostate cancer models and a complete reduction of α-synuclein accumulation in Parkinson's disease models. These activities could be harnessed in the clinic or open new drug discovery opportunities. The study reported here highlights the importance of characterizing the activity of drug metabolites to comprehensively understand drug response in the clinic and exploit our current drug arsenal in precision medicine.

Bipolar, high-voltage, long-duration pulsed radiofrequency ablation of the Gasserian ganglion for the treatment of trigeminal neuralgia in a patient with a cardiac implantable electronic device: illustrative case.

BACKGROUND: One of the common methods of treating trigeminal neuralgia (TN) nowadays is radiofrequency therapy. However, it has serious limitations in patients with a cardiac pacemaker because of electromagnetic interference. Therefore, it is crucial to select optimal radiofrequency ablation parameters to make this procedure safe with favorable outcomes for such patients. OBSERVATIONS: In this study, the authors present a case of a 70-year-old man with a history of cardiac pacemaker dependency and previous microvascular decompression with complaints of severe, constant facial pain. After reprogramming the cardiac implantable electronic device (CIED), the authors performed bipolar, high-voltage, long-duration pulsed radiofrequency therapy (PRFT) of the Gasserian ganglion under electrocardiography and pulse rate control in the pre-, intra-, and postoperative periods. There were no cardiovascular or neurological complications after PRFT. The patient reported relief of pain after the procedure, and at the 9-month follow-up, he was pain-free. LESSONS: This clinical case demonstrates that the use of bipolar, high-voltage PRFT for TN treatment in patients with a CIED can be safe and effective, provided that the rules and pacemaker instructions are followed. It is necessary to use ablative treatment with caution and to guide the patient in collaboration with a cardiac surgeon and an anesthesiologist resuscitator.

Temperature-Dependent Kinetics for the Reactions of Fen- (n = 2-17) and FexNiy- (x + y = 3-9) with O2: Comparison of Pure and Mixed Metal Clusters with Relevance to Meteor Radio Afterglows and Surface Oxidation.

Rate constants and product branching fractions were measured from 300-600 K for Fen- + O2 (n = 2-17) and for 300-500 K for FexNiy- + O2 (x + y = 3-9) using a selected-ion flow tube (SIFT) apparatus. Rate constants for 46 species are reported. All rate constants increased with increasing temperature, and several were in excess of the Langevin-Gioumousis-Stevenson (LGS) capture rate at elevated temperatures. As with previously studied transition metal anion oxidation reactions, the collision limit is treated as the sum of the LGS limit along with a hard-sphere contribution, allowing for determination of activation energies. These values are compared to each other along with previous results for Nin-. Measured rate constants for all three series (Fen-, Nin-, and FexNy-) vary over a relatively narrow range (1-5 × 10-10 cm3 s-1 at 300 K) being at least 15% of the collision rate constant. All reaction rate constants increase with temperature, described by small activation energies of 0.5-4 kJ mol-1. The data are consistent with an anticorrelation between the electron binding energy and rate constant, previously noted in other systems. The Fen- reaction produces a larger population of higher energy electrons than do the Nin- reactions, with FexNiy- producing an intermediate amount. The results suggest that the overall rate constant is limited by a small energetic barrier located at a large internuclear distance where electrostatic forces dominate, causing the potentials to be similar across systems, while the product formation is determined by the shorter-range, valence portion of the potential, which varies widely between systems.