Optimal Sequence Research Articles

Optimization of quenched fluorescent peptide substrates of SARS-CoV-2 3CLpro main protease (Mpro) from proteomic identification of P6-P6' active site specificity.

SARS-CoV-2 3C-like main protease (3CLpro) is essential for protein excision from the viral polyprotein. 3CLpro inhibitor drug development to block SARS-CoV-2 replication focuses on the catalytic non-prime (P) side for specificity and potency, but the importance of the prime (P') side in substrate specificity and for drug development remains underappreciated. We determined the P6-P6' specificity for 3CLpro from >800 cleavage sites that we identified using Proteomic Identification of Cleavage site Specificity (PICS). Cleavage occurred after the canonical P1-Gln and non-canonical P1-His and P1-Met residues. Moreover, P3 showed a preference for Arg/Lys and P3' for His. Essential H-bonds between the N-terminal Ser1 of protomer-B in 3CLpro dimers form with P1-His, but not with P1-Met. Nonetheless, cleavage occurs at P1-Met456 in native MAP4K5. Elevated reactive oxygen species in SARS-CoV-2 infection oxidize methionines. Molecular simulations revealed P1-MetOX forms an H-bond with Ser1 and notably, strong positive cooperativity between P1-Met with P3'-His was revealed, which enhanced peptide-cleavage rates. The highly plastic S3' subsite accommodates P3'-His that displays stabilizing backbone H-bonds with Thr25 lying central in a "'threonine trio" (Thr24-Thr25-Thr26) in the P'-binding domain I. Molecular docking simulations unveiled structure-activity relationships impacting 3CLpro-substrate interactions, and the role of these structural determinants was confirmed by MALDI-TOF-MS cleavage assays of P1'- and P3'-positional scanning peptide libraries carrying a 2nd optimal cut-site as an internal positive control. These data informed the design of two new and highly soluble 3CLproquenched-fluorescent peptide substrates for improved FRET monitoring of 3CLpro activity with 15× improved sensitivity over current assays.IMPORTANCEFrom global proteomics identification of >800 cleavage sites, we characterized the P6-P6' active site specificity of SARS-CoV-2 3CLpro using proteome-derived peptide library screens, molecular modeling simulations, and focussed positional peptide libraries. In P1', we show that alanine and serine are cleaved 3× faster than glycine and the hydrophobic small amino acids Leu, Ile, or Val prevent cleavage of otherwise optimal non-prime sequences. In characterizing non-canonical non-prime P1 specificity, we explored the unusual P1-Met specificity, discovering enhanced cleavage when in the oxidized state (P1-MetOX). We unveiled unexpected amino acid cooperativity at P1-Met with P3'-His and noncanonical P1-His with P2-Phe, and the importance of the threonine trio (Thr24-Thr25-Thr26) in the prime side binding domain I in defining prime side binding in SARS-CoV-2 3CLpro. From these analyses, we rationally designed quenched-fluorescence natural amino acid peptide substrates with >15× improved sensitivity and high peptide solubility, facilitating handling and application for screening of new antiviral drugs.

Nonlinear Model Predictive Control with Evolutionary Data-Driven Prediction Model and Particle Swarm Optimization Optimizer for an Overhead Crane

This paper presents a new approach to the nonlinear model predictive control (NMPC) of an underactuated overhead crane system developed using a data-driven prediction model obtained utilizing the regularized genetic programming-based symbolic regression method. Grammar-guided genetic programming combined with regularized least squares was applied to identify a nonlinear autoregressive model with an exogenous input (NARX) prediction model of the crane dynamics from input–output data. The resulting prediction model was implemented in the NMPC scheme, using a particle swarm optimization (PSO) algorithm as a solver to find an optimal sequence of the control actions satisfying multi-objective performance requirements and input constraints. The feasibility and performance of the controller were experimentally verified using a laboratory crane actuated by AC motors and compared with a discrete-time feedback controller developed using the pole placement technique. A series of experiments proved the effectiveness of the controller in terms of robustness against operating condition variation and external disturbances.

Optimization of cut-off grades and depletion rates by means of dynamic programming

The determination of cut-off grades is critical for mine appraisal. Because of the time value of money, an optimum schedule of cut-off grades must be used instead of determination of a single cut-off grade for the life of a mine. In order to achieve the maximum net present value, a decreasing order of cut-off grades schedule must be used because of the opportunity cost. In the optimization of cut-off grades by dynamic programming, the depletion rates to be searched are actually limiting the search area. The optimum can be found from among the alternative depletion rates. Selling prices of mining products are volatile and future values cannot be known. This paper describes the use of dynamic programming in cut-off grades optimization and further extension of the method to price volatility in selling prices of mining products. The author introduces the general background of use of dynamic programming in cut-off grades optimization and the case in uncertain market conditions. Software developed in this subject handles to find the optimum sequence of cut-off grades and depletion rates for mineral deposits under market uncertainty. Dynamic programming method can be used as a means of cut-off grades optimization in this context very robustly.

The narrative crisis model of suicide as a framework for suicide prevention

The Narrative Crisis Model of Suicide (NCM) is an iterative, dynamic, diathesis-stress model that conceptualizes individuals’ progression to suicidal behavior through the incorporation of four distinct and successive stages. The NCM strives to reflect a paradigm change in the prevention of suicide: rather than assessing individuals’ suicide risk and categorizing them along different risk levels based on their self-reported suicidal ideation at a given time, the NCM proposes to assess psychological vulnerabilities and processes characteristic of critical milestones in the progression from chronic factors to acute suicidal mental states. As a comprehensive model, the NCM provides an empirically grounded conceptual framework for intervention at each stage of the psychological progression towards suicidal action. In this article, we propose stage-specific treatment modalities, moving progressively from acute to chronic risk. Such treatments would first target the acute Suicide Crisis Syndrome (Stage 4), the subacute suicidal narrative (Stage 3), deficits in stress management (Stage 2), and finally long-term risk factors/trait vulnerabilities (Stage 1). Although future research is needed to establish the optimal combination and sequence of empirically-supported interventions, the NCM may be a useful framework to guide innovations in clinical intervention and research.

A Study on the Effects of Welding Deformation According to Weld Sequence in Overlay-Welded Structures

In this study, thermal elasto-plastic finite element analysis was conducted to derive the optimal welding sequence to minimize overlay welding deformation on the water wall panels of an SRF (solid refuse fuel) boiler. The water wall panels of an SRF boiler are exposed to high temperatures and corrosive environments, making overlay welding essential. However, because the length of the water wall panels and tubes exceeds 7 m, significant deformation occurs after overlay welding. Additionally, due to the large size of the water wall panels, full-size thermal elasto-plastic analysis requires huge computational costs. Therefore, in this study, the effects of welding sequence on overlay welding deformation were first investigated for a reduced model to derive the optimal welding sequence. Subsequently, an analysis model for the full-size pipe panels was established to compare and analyze the conventional welding sequence with the optimal welding sequence, thereby verifying the validity of the study. According to the welding sequence derived from the reduced model, welding deformation in the full-size model was significantly reduced compared to the conventional sequence. This reduction in deformation was discussed by analyzing the deformation behavior of the structure at each stage of the overlay welding process.

A maximum entropy deep reinforcement learning method for sequential well placement optimization using multi-discrete action spaces

Well placement optimization is a crucial method to solve the planar conflicts in reservoir development, mainly to determine the optimal well locations and drilling sequence to maximize the economic benefits of reservoir development. However, the current well placement optimization methods face the problems of high-dimensional discretization of optimization variables and lack of effective incentives for policy exploration, which make it challenging to improve the global optimization ability (the ability to jump out of locally optimal solutions in time and continuously search for better solutions in the whole optimization process) and real-time adjustability of the well placement optimization methods under limited numerical simulation times. In this paper, we propose a new sequential well placement optimization method, based on the Discrete Soft Actor-Critic Algorithm (DSAC), which incorporates the maximum entropy mechanism to formulate well placement and drilling sequencing schemes more efficiently and maximize the net present value (NPV) over the entire life cycle of reservoir development. Specifically, the method models the well placement optimization problem as a Markov Decision Process (MDP) and achieves sequential well placement optimization by training a Deep Reinforcement Learning (DRL) agent that maps reservoir states to a stochastic policy of well placement variables as well as evaluates the value function of the current policy. The DRL agent can determine the optimal infill well location in real-time based on the reservoir state at different times during the development process, thus obtaining the optimal drilling sequence. The proposed method in this paper has two innovations. First, by reconstructing the large-scale discrete action space of well placement optimization variables into multi-discrete action spaces, and with the maximum entropy mechanism, policy exploration is encouraged to improve the global optimization capability. Second, the trained policy can swiftly adapt the subsequent well placement scheme for a specific state of the target reservoir without the requirement to initiate training from scratch, which can realize the offline application of the trained policy and has better real-time adjustability. To verify the effectiveness of the proposed method, it is tested in 2D and 3D reservoir models. The results show that DSAC not only outperforms the gradient-based optimization method, classical evolutionary algorithms, and existing reinforcement learning proximal policy optimization (PPO) method in terms of global optimization ability but also shows better real-time adjustability of the trained policy when applied offline.

Antibody–drug conjugates (ADCs) in lung cancer treatment

SummaryADCs will likely become another important treatment strategy in advanced, pretreated lung cancer where current treatment options are limited. Both onocogene-directed or biomarker relateted (e.g. EGFR, HER2, MET) and ADCs where no biomarker selection is required (e.g. TROP2, HER3) have either already demonstrated efficacy or under further investigation in NSCLC. Improving the outcomes of patients with metastatic lung cancer is important, thus continued research is necessary to improve understanding of the clinical activity, safety, and optimal sequencing of ADCs in lung cancer treatment.

Covalent Flexible Peptide Docking in Drug Discovery: Current Challenges and Potential Interventions

Recently, there has been a significant increase in the search for new covalent inhibitors through drug discovery platforms, which has led to the development and implementation of new computational tools, including covalent docking methods to predict the binding mode and affinity of covalent ligands. Since the discovery of insulin nearly a century ago, more than 80 peptide medications have hit the market to treat a wide range of diseases, including diabetes, cancer, osteoporosis, multiple sclerosis, HIV infection, and chronic pain. Electrophilic peptides that form covalent bonds with target proteins have great potential for binding targets that have been previously considered undruggable. Despite the recent advancements in computational performance and docking algorithms, covalent docking still poses a number of challenges. Peptides covalent docking presents additional challenges, the main ones being the choice of the optimal peptide sequence, incorporation of electrophilic warheads, the inherent flexibility of peptide structures, and the fact that, unlike small molecules, peptides do fold. In this review, we present a brief overview of the current state of peptide therapeutics in drug discovery, covalent docking in general, covalent peptide binders, and-with particular emphasis-the difficulties that covalent peptide-protein docking is currently facing and some potential solutions.

Order sequencing, tote scheduling, and robot routing optimization in multi-tote storage and retrieval autonomous mobile robot systems

This study explores a novel multi-tote storage and retrieval autonomous mobile robot system, where multi-tote autonomous mobile robots transport totes (or ‘SKU bins’) for order picking. We investigate the joint optimisation problem of order sequencing, tote scheduling, and robot routing in a single workstation equipped with the capacity to accommodate multiple order bins and parallel totes. The inbound and outbound streams of SKUs stored in totes are crucial for order picking at the workstation, as they jointly affect the picking performance efficiency through synchronization with the order sequence. To address this synchronization problem, we formulate a Mixed-Integer Linear Programming (MILP) model and propose a two-stage hybrid heuristic combining a variable neighbourhood search (VNS) algorithm and a refinement model. This model further improves the VNS solution with partially fixed SKU inbound stream and problem-tailored inequalities. Our numerical studies highlight the superior performance of the proposed hybrid heuristic, where the VNS solution surpasses the benchmark VNS-Simplified by a significant margin of 14%. The rearrangement process enhances the VNS performance by reducing 33.8% of SKU revisits. We also offer managerial insights, suggesting that the maximum number of order bins and parallel totes, exhibit boundary points where adding capacity yields limited marginal improvements.

LPI Sequences Optimization Method against Summation Detector Based on FFT Filter Bank

Waveform design is a crucial factor in electronic surveillance (ES) systems. In this paper, we introduce an algorithm that designs a low probability of intercept (LPI) radar waveform. Our approach directly minimizes the detection probability of summation detectors based on FFT filter banks. The algorithm is derived from the general quadratic optimization framework, which inherits the monotonic properties of such methods. To expedite overall convergence, we have integrated acceleration schemes based on the squared iterative method (SQUAREM). Additionally, the proposed algorithm can be executed through fast Fourier transform (FFT) operations, enhancing computational efficiency. With some modifications, the algorithm can be adjusted to incorporate spectral constraints, increasing its flexibility. Numerical experiments indicate that our proposed algorithm outperforms existing ones in terms of both intercept properties and computational complexity.

A Novel Dose Rate Optimization Method to Maximize Ultrahigh-Dose-Rate Coverage of Critical Organs at Risk Without Compromising Dosimetry Metrics in Proton Pencil Beam Scanning FLASH Radiation Therapy

PurposeTo investigate a dose rate optimization framework based on the spot scanning patterns to improve ultra-high dose rate coverage of critical organs-at-risk (OARs) for proton PBS FLASH radiotherapy, and to present implementation of a genetic algorithm (GA) method for spot sequence optimization to achieve PBS FLASH dose rate optimization under relatively low nozzle beam currents. Materials and MethodsFirstly, a multi-field FLASH plan was developed to meet all the dosimetric goals and optimal FLASH dose rate coverage by considering the deliverable minimum MU (MMU) constraint. Then, a GA method was implemented into the in-house treatment platform to maximize the dose rate by exploring the best spot delivery sequence. A phantom study was performed to evaluate the effectiveness of the dose rate optimization. Then, 10 consecutive plans for lung cancer patients previously treated using PBS intensity-modulated proton therapy (IMPT) were optimized using 45 GyRBE in 3 fractions both transmission and Bragg peak FLASH-RT for further validation. The spot delivery sequence of each treatment field was optimized using this GA. The ultra-high dose rate volume histogram (DRVH) and dose rate coverage V40GyRBE/s were investigated to assess the efficacy of dose rate optimization quantitatively. ResultsUsing a relatively low MU/spot of 150, corresponding to nozzle beam current of 65 nA, the FLASH dose rate ratio V40GyRBE/s of the OAR contour of the core was increased from 0 to ∼60% in the phantom study. In the lung cancer patients, the ultra-high dose rate coverage V40GyRBE/s were improved from 15.2%, 15.5%, 17.6%, and 16.0% before the delivery sequence optimization, to 31.8%, 43.5%, 47.6%, and 30.5% after delivery sequence optimization, in the lungs-GTV, spinal cord, esophagus, and heart (p-values all<0.001). When beam current increased to 130 nA, V40GyRBE/s was improved from 45.1%, 47.1%, 51.2%, and 51.4%, to 65.3%, 83.5%, 88.1%, and 69.4% (p-values<0.05). The averaged V40GyRBE/s for the target and OARs was increased from 12.9% to 41.6%, and 46.3% to 77.5% for 65 and 130 nA, respectively, showing significant improvements based on a clinical proton system. After optimizing the dose rate for the Bragg peak FLASH technique with a beam current of 340 nA, the V40GyRBE/s for the lung-GTV, spinal cord, esophagus, and heart were increased by 8.9%, 15.8%, 22%, and 20.8%, respectively. ConclusionAn optimal plan quality can be maintained as the spot delivery sequence optimization is a separate independent process after the plan optimization. Both the phantom and patient results demonstrated that novel spot delivery sequence optimization can effectively improve the ultra-high dose rate coverage for critical OARs, which can potentially be applied in clinical practice for better OARs sparing efficacy.

Advancements in Urothelial Cancer Care: Optimizing Treatment for Your Patient.

The standard treatment paradigm for muscle invasive bladder cancer has been neoadjuvant cisplatin-based chemotherapy followed by radical cystectomy. However, efforts are ongoing to personalize treatment by incorporating biomarkers to better guide treatment selection. In addition, bladder preservation strategies are aimed at avoiding cystectomy in well-selected patients. Similarly, in the metastatic urothelial cancer space, the standard frontline treatment option of platinum-based chemotherapy has changed with the availability of data from EV-302 trial, making the combination of enfortumab vedotin (EV) and pembrolizumab the preferred first-line treatment option. Here, we examine the optimization of treatment intensity and sequencing, focusing on the challenges and opportunities associated with EV/pembrolizumab therapy, including managing toxicities and exploring alternative dosing approaches. Together, these articles provide a comprehensive overview of contemporary strategies in bladder cancer management, highlighting the importance of individualized treatment approaches, ongoing research, and multidisciplinary collaboration to improve patient outcomes in this complex disease landscape.

Research on the Digital Maintenance Construction of Hydropower Unit

Abstract At present, the single unit capacity of hydropower unit is increasing, and the problems of difficulty, high precision, large number of labors and high cost in the maintenance process of large unit are becoming increasingly prominent. With the proposal of intelligent construction of hydropower plant, digital maintenance of hydropower unit has become a research hotspot. This article proposes a construction idea for a digitized maintenance system for hydraulic units based on technologies such as RFID, IoT, and artificial intelligence. This system can modularly manage the unit components and realize the integration and interaction of unit maintenance information through the visualization platform. It utilizes IoT sensors to collect comprehensive information on the components involved in the maintenance process. The components’ information is managed in a hierarchical manner using device KKS coding and a hierarchical structure diagram of the hydraulic unit’s equipment components. Through a visualization platform and equipment knowledge base, the component information is processed and analyzed to generate optimal assembly sequences and paths, thus improving the scientific and rational aspects of the unit’s maintenance process. This approach provides theoretical support for maintenance operations and enhances overall work efficiency.

Charting the Course: Sequencing Immunotherapy for Multiple Myeloma.

Multiple chimeric antigen receptor (CAR) T-cell and bispecific antibody (bsAb) therapies have been approved, demonstrating impressive clinical efficacy in relapsed/refractory multiple myeloma (MM). Currently, these treatment share overlapping approval indications in the relapsed/refractory space, highlighting the importance of optimal selection and sequencing to maximize clinical efficacy. For patients previously unexposed to T-cell-directed therapies, several factors should be weighed when both options are available. These factors include access and logistical challenges associated with CAR T-cell therapy, disease-specific factors such as tempo of disease relapse, in addition to patient-specific factors such as frailty, and distinct toxicity profiles across these agents. Sequential therapy, whether it involves CAR T-cell therapy followed by bsAb or vice versa, has demonstrated clinical efficacy. When sequencing these agents, it is crucial to consider various factors that contribute to treatment resistance with careful selection of treatments for subsequent therapy in order to achieve favorable long-term patient outcomes.

Clinical and molecular characteristics and targeted therapy of pediatric non-infantile fibrosarcoma NTRK rearrangement–related tumors.

10048 Background: Neurotrophic tyrosine receptor kinase (NTRK) rearrangement is rare in tumors and relevant to targeted therapy. Pediatric NTRK rearrangement-related tumors have distinct clinical and molecular features compared to adults, and previous studies on pediatric NTRK rearrangement-related tumors has focused on infantile fibrosarcoma (IFS), the most common subtype. However, the clinical behaviors and molecular profiles of other subtypes of pediatric NTRK rearrangement-related tumors are largely unknown. Methods: In this study, we evaluated the molecular features, treatment response, and prognosis of 28 pediatric patients with non-IFS NTRK rearrangement-related tumors. Next-generation sequencing was performed in 21 patients to identify the NTRK fusion types and their co-occurring alterations, and pathway enrichment analysis was performed to explore the potential mechanisms involving NTRK rearrangement and resistance to tyrosine receptor kinase (TRK) inhibitors. Results: The NTRK fusion partners were diverse and tumor-specific. Several novel NTRK fusions were identified, including SNIP1-NTRK1, AGAP1-NTRK2, CRCT1-NTRK1, ZBTB7B-NTRK1, and SLC6A15-NTRK3. RTK-RAS, NOTCH, TP53, and WNT pathways were top 4 significantly enriched in tested samples. Twelve of 17 patients (70.6%) received TRK inhibitors as salvage treatment achieved complete or partial response. Patients who did not develop DDR, NOTCH, or TP53 pathway-related gene mutations had a high objective response rate of 82%, 82%, and 83%, respectively. Conclusions: Our findings may have important implications for the diagnosis and treatment of non-IFS NTRK fusion-positive tumors in pediatric patients. We also provide potential therapeutic targets for tumors resistant to TRK inhibitors. Clinical trials with TRK inhibitors, either alone or in combination, are required to establish the optimal treatment regimen and sequence in large-cohort pediatric patients with non-IFS NTRK rearrangement-related tumors. Clinical trial information: NCT05076071 .

Evolving Standards of Care in the Management of Localized Colorectal Cancer.

The treatment of patients with localized rectal cancer is complex and requires input from a multidisciplinary team. Baseline local staging and mismatch repair protein testing are vital to develop individualized treatment plans. There are multiple options in terms of treatment modalities and sequencing, including transanal excision, short-course radiation, long-course chemoradiation, chemotherapy doublet or triplet, nonoperative management, and immune checkpoint blockade for patients with mismatch repair deficient tumors. While localized colon cancer is typically treated with surgical resection and consideration of adjuvant chemotherapy, emerging data suggest that neoadjuvant chemotherapy may be beneficial in patients with higher-risk disease. Quality-of-life considerations are imperative to prevent potential chronic effects on psychosocial health, neuropathy, fertility, and bowel, bladder, and sexual function. The omission of radiation or surgery can mitigate these toxicities without diminishing oncologic outcomes. The optimal treatment plan and sequence is not a one-size-fits-all approach but rather should be personalized to the patient's disease burden, tumor location, comorbidities, and preferences.

Simulation and optimization of crude glycerol refining process as a co-product of biodiesel

Glycerol is an essential chemical raw material widely used in industries such as food, pharmaceuticals, cosmetics, tobacco, and textiles. With the increasing production of biodiesel, a considerable amount of crude glycerol co-product is generated. Purification of crude glycerol by separating it from biodiesel co-products can lead to the production of high-purity glycerol, thus enabling the synthesis of high-value-added chemicals to reduce the production cost of biodiesel. This paper proposes a new glycerol purification process based on the acidification of potassium oleate soap using sulphuric acid. The composition of crude glycerol is complex. The presence of salts can affect the binary interaction parameters of glycerol and water. The ion forms resulting from the ionization of potassium fatty acid soaps are not available in the database and require important data to be provided through physical property estimation. Additionally, the dissociation of sulphuric acid is involved in the system. These issues make it challenging to propose a complete process based on the characteristics of crude glycerol. In this study, the vapor–liquid equilibrium data of the glycerol-water system containing potassium sulphate under reduced pressure were experimentally determined, and the binary interaction parameters of the glycerol-water system were regressed and used in the subsequent process simulation. The entire process of crude glycerol refining from biodiesel co-products was simulated and optimized using Aspen Plus software, proposing an optimized complete refining process for crude glycerol and designing a crude glycerol refining process with a production scale of 100,000 tons per year. The potassium oleate soap is first acidified with sulphuric acid, then removed by centrifugation, with the resulting lower layer entering the distillation process. We compared two different distillation processes and identified the more optimal separation sequence. Glycerol products with a mass concentration of 99.2% were obtained, with glycerol and methanol yields of 98.83% and 99.79%, respectively. Based on the above research, energy integration and optimization of the process were further investigated through pinch analysis. Overall, energy savings of 7.192 Gcal/h were achieved, resulting in a reduction in energy consumption of 40.65%. This paper proposes a new purification scheme for crude glycerol and an energy integration scheme to reduce the purification cost of high-purity glycerol. This is significant for the rational utilization of crude glycerol, a co-product of biodiesel.

Association between rare pathogenic variants in established cancer risk genes and the diagnosis of single and multiple common cancers: A UK Biobank study.

10505 Background: Much of our understanding of cancer risk attributable to rare pathogenic variants (RPV) is derived from family-based and case-only cohort studies, which may be limited by ascertainment bias. We sought to identify associations between RPVs and cancer diagnoses in a large population-based study. Methods: We conducted a case-control study using the UK Biobank. We performed gene-based aggregate testing to examine the relationship between RPVs (defined as pathogenic or likely pathogenic variants) in 96 genes implicated in cancer risk and the diagnosis of 11 common cancers using the combined optimal unified sequence kernel association test (SKAT-O). Odds ratios (OR) and 95% confidence intervals (CI) were then calculated using Firth logistic regression. Results were adjusted for age, sex and genetic ancestry. Bonferroni-adjusted p-values were used to determine statistical significance. Variant pathogenicity was determined by American College of Medical Genetics criteria. Results: We identified 24 genes in which RPVs were statistically significantly associated with at least one of 11 common cancers among 183,626 individuals. The presence of an RPV in one of these 24 genes was associated with increased odds of one cancer (OR 1.8; 95% CI: 1.7-2.0), and multiple cancers (OR 2.5; 95% CI: 2.2-2.9). Conclusions: This large population-based study identified 24 established cancer risk genes in which RPVs were associated with common cancers. We identified both known and novel associations between cancer and RPVs. Some associations—i.e. renal/pancreas and MEN1—may be due to ascertainment and/or misclassification bias. We also show that individuals with RPVs in cancer risk genes not only have higher odds of one cancer diagnosis, but also multiple cancers.[Table: see text]

Comprehensive Multiple Indicators Optimizations for Two-Parallel Interleaved Three-Phase Two-Level Power Converters Using Optimal Vector Sequences

Comprehensive Multiple Indicators Optimizations for Two-Parallel Interleaved Three-Phase Two-Level Power Converters Using Optimal Vector Sequences

Informing optimal treatment (tx) sequences for platinum-eligible patients with locally advanced or metastatic urothelial carcinoma (la/mUC) in the United States: Results of a clinical simulation modeling exercise.

e13514 Background: Platinum-based chemotherapy (PBT) followed by avelumab (AVE) as first-line (1L) maintenance for patients with nonprogressive disease (PBT ± AVE) is the standard of care in la/mUC. This study compared the overall survival (OS) and cost-effectiveness of the tx sequence of PBT ± AVE followed by enfortumab vedotin (EV) or other therapies, including immuno-oncology (IO), in second line (2L; ie, PBT + 2L EV/IO or PBT + AVE + 2L EV/IO) vs 1L EV + pembrolizumab (PEM) for PBT-eligible patients (PBTp) from a US commercial payer perspective. Methods: A partitioned survival model compared outcomes with PBT ± AVE and EV + PEM among PBTp. Inputs included progression-free survival and OS, time to tx discontinuation, adverse event (AE) incidence, and health utilities and costs. Efficacy inputs were sourced and extrapolated from patient data in JAVELIN Bladder 100 (data cut: Jun 4, 2021), AVENANCE real-world study (1), and published data (EV-302 trial). Safety data were sourced from US prescribing information. Drug, subsequent tx, disease management, and AE management costs (2023 USD) were calculated. The optimal tx sequencing scenario assumed 80% of patients starting PBT were eligible to receive AVE (2023 Flatiron data cut; (2)), 70% of patients progressing on PBT ± AVE would start 2L, and 70% of those would be eligible for EV. Outcomes included life-years (LYs), quality-adjusted LYs (QALYs), costs, and incremental cost-effectiveness ratio (ICER). Scenarios and probabilistic sensitivity analyses (PSAs) were conducted to assess uncertainty. Results: LY, QALY, costs, and ICER modeling outcomes are shown in the Table. Use of EV in 2L after PBT ± AVE is associated with 0.76 lower QALYs, but given the large cost saving, it represents a cost-effective use of resources at the $150,000/QALY willingness to pay (WTP) threshold. PSAs showed that PBT ± AVE is likely to be more cost-effective over a broad and relevant WTP threshold range ($0-500,000/QALY). Results were sensitive to drug acquisition costs and eligibility to receive AVE and 2L EV. Conclusions: Based on this simulation exercise, the use of 2L EV after PBT ± AVE achieves considerable survival benefit at substantially lower costs vs 1L EV + PEM in PBTp with la/mUC. Given the shift toward value-based care in oncology, such analyses are becoming increasingly relevant to guide clinical decision-making. There is a need to assess tx alternatives that balance healthcare costs while optimizing outcomes in patients with la/mUC. 1. Barthélémy et al, J Clin Oncol 2024;42[suppl 4]: Abstract 561. 2. Moon et al, J Clin Oncol 2023;41[suppl 16]: Abstract 4567. [Table: see text]