scholarly journals Multiobjective optimization identifies cancer-selective combination therapies

2020 ◽  
Vol 16 (12) ◽  
pp. e1008538
Author(s):  
Otto I. Pulkkinen ◽  
Prson Gautam ◽  
Ville Mustonen ◽  
Tero Aittokallio
Keyword(s):  
Multiobjective Optimization ◽  
Cost Effective ◽  
Selective Inhibition ◽  
Search Space ◽  
Optimization Method ◽  
Drug Combinations ◽  
Braf V600e ◽  
Patient Specific ◽  
Precision Oncology ◽  
Massive Number

Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient, using cost-effective methods. Here, we developed an exact multiobjective optimization method for identifying pairwise or higher-order combinations that show maximal cancer-selectivity. The prioritization of patient-specific combinations is based on Pareto-optimization in the search space spanned by the therapeutic and nonselective effects of combinations. We demonstrate the performance of the method in the context of BRAF-V600E melanoma treatment, where the optimal solutions predicted a number of co-inhibition partners for vemurafenib, a selective BRAF-V600E inhibitor, approved for advanced melanoma. We experimentally validated many of the predictions in BRAF-V600E melanoma cell line, and the results suggest that one can improve selective inhibition of BRAF-V600E melanoma cells by combinatorial targeting of MAPK/ERK and other compensatory pathways using pairwise and third-order drug combinations. Our mechanism-agnostic optimization method is widely applicable to various cancer types, and it takes as input only measurements of a subset of pairwise drug combinations, without requiring target information or genomic profiles. Such data-driven approaches may become useful for functional precision oncology applications that go beyond the cancer genetic dependency paradigm to optimize cancer-selective combinatorial treatments.

scholarly journals Multiobjective Optimization Identifies Cancer-Selective Combination Therapies

2020 ◽  
Author(s):  
Otto I. Pulkkinen ◽  
Prson Gautam ◽  
Ville Mustonen ◽  
Tero Aittokallio
Keyword(s):  
Multiobjective Optimization ◽  
Cost Effective ◽  
Selective Inhibition ◽  
Search Space ◽  
Optimization Method ◽  
Drug Combinations ◽  
Braf V600e ◽  
Patient Specific ◽  
Precision Oncology ◽  
Massive Number

ABSTRACTCombinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient, using cost-effective methods. Here, we developed an exact multiobjective optimization method for identifying pairwise or higher-order combinations that show maximal cancer-selectivity. The prioritization of patient-specific combinations is based on Pareto-optimization in the search space spanned by the therapeutic and nonselective effects of combinations. We demonstrate the performance of the method in the context of BRAF-V600E melanoma treatment, where the optimal solutions predicted a number of co-inhibition partners for vemurafenib, a selective BRAF-V600E inhibitor, approved for advanced melanoma. We experimentally validated many of the predictions in BRAF-V600E melanoma cell line, and the results suggest that one can improve selective inhibition of BRAF-V600E melanoma cells by combinatorial targeting of MAPK/ERK and other compensatory pathways using pairwise and third-order drug combinations. Our mechanism-agnostic optimization method is widely applicable to various cancer types, and it takes as input only measurements of a subset of pairwise drug combinations, without requiring target information or genomic profiles. Such data-driven approaches may become useful for functional precision oncology applications that go beyond the cancer genetic dependency paradigm to optimize cancer-selective combinatorial treatments.

scholarly journals Precision Genomic Practice in Oncology: Pharmacist Role and Experience in an Ambulatory Care Clinic

Pharmacy ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 32 ◽  
Author(s):  
Farah Raheem ◽  
Pauline Kim ◽  
Meagan Grove ◽  
Patrick J. Kiel
Keyword(s):  
Clinical Trial Design ◽  
Cost Effective ◽  
Standard Of Care ◽  
The United States ◽  
Tumor Board ◽  
Patient Specific ◽  
Cancer Center ◽  
Molecular Testing ◽  
Precision Oncology ◽  
Care Clinic

Recent advancements in molecular testing, the availability of cost-effective technology, and novel approaches to clinical trial design have facilitated the implementation of tumor genome sequencing into standard of care oncology practices. Current models of precision oncology practice include specialized clinics or consultation services based on a molecular tumor board (MTB) approach. MTBs are comprised of interprofessional teams of clinicians and scientists who evaluate tumors at the molecular level to guide patient-specific targeted therapy. The practice of precision oncology utilizing MTB-based models is an emerging approach, transforming precision genomics from a novel concept into clinical practice. This rapid shift in practice from cytotoxic therapy to targeted medicine poses challenges, yet brings exciting opportunities to clinical pharmacists practicing in hematology and oncology. Only a few precision genomics programs in the United States have a strong pharmacy presence with oncology pharmacists serving in leadership roles in research, interpreting genomic sequencing, making treatment recommendations, and facilitating off-label drug procurement. This article describes the experience of the precision medicine clinic at the Indiana University Health Simon Cancer Center, with emphasis on the role of the pharmacist in the precision oncology initiative.

An evaluation of a shoulder rehabilitation class in a UK hospital following evidence-based modifications

2021 ◽  
pp. 1-8
Author(s):  
Emily Kell ◽  
John A. Hammond ◽  
Sophie Andrews ◽  
Christina Germeni ◽  
Helen Hingston ◽  
...  
Keyword(s):  
Patient Satisfaction ◽  
Shoulder Pain ◽  
Management Strategies ◽  
Cost Effective ◽  
Rehabilitation Programme ◽  
Patient Specific ◽  
Satisfaction Survey ◽  
Evidence Based ◽  
Patient Satisfaction Survey ◽  
Significant Difference

OBJECTIVES: Shoulder pain is a common musculoskeletal disorder, which carries a high cost to healthcare systems. Exercise is a common conservative management strategy for a range of shoulder conditions and can reduce shoulder pain and improve function. Exercise classes that integrate education and self-management strategies have been shown to be cost-effective, offer psycho-social benefits and promote self-efficacy. This study aimed to examine the effectiveness of an 8-week educational and exercise-based shoulder rehabilitation programme following the introduction of evidence-based modifications. METHODS: A retrospective evaluation of a shoulder rehabilitation programme at X Trust was conducted, comparing existing anonymised Shoulder Pain and Disability Index (SPADI) and Patient-Specific Functional Scale (PSFS) scores from two cohorts of class participants from 2017-18 and 2018-19 that were previously collected by the physiotherapy team. Data from the two cohorts were analysed separately, and in comparison, to assess class efficacy. Descriptive data were also analysed from a patient satisfaction survey from the 2018-19 cohort. RESULTS: A total of 47 patients completed the 8-week shoulder rehabilitation programme during the period of data collection (2018-2019). The 2018-19 cohort showed significant improvements in SPADI (p 0.001) and PSFS scores (p 0.001). No significant difference was found between the improvements seen in the 2017-18 cohort and the 2018-19 cohort. 96% of the 31 respondents who completed the patient satisfaction survey felt the class helped to achieve their goals. CONCLUSION: A group-based shoulder rehabilitation class, which included loaded exercises and patient education, led to improvements in pain, disability and function for patients with rotator cuff related shoulder pain (RCRSP) in this outpatient setting, but anticipated additional benefits based on evidence were not observed.

scholarly journals Predictive Evaluation on Cytological Sample of Metastatic Melanoma: The Role of BRAF Immunocytochemistry in the Molecular Era

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1110
Author(s):  
Andrea Ronchi ◽  
Marco Montella ◽  
Federica Zito Marino ◽  
Michele Caraglia ◽  
Anna Grimaldi ◽  
...  
Keyword(s):  
Metastatic Melanoma ◽  
Molecular Analysis ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Cost Effective ◽  
Needle Aspiration ◽  
Braf V600e ◽  
Mutational Status ◽  
Needle Aspiration Cytology ◽  
Sensitivity Specificity

Background: Cutaneous malignant melanoma is an aggressive neoplasm. In advanced cases, the therapeutic choice depends on the mutational status of BRAF. Fine needle aspiration cytology (FNA) is often applied to the management of patients affected by melanoma, mainly for the diagnosis of metastases. The evaluation of BRAF mutational status by sequencing technique on cytological samples may be inconvenient, as it is a time and biomaterial-consuming technique. Recently, BRAF immunocytochemistry (ICC) was applied for the evaluation of BRAF V600E mutational status. Although it may be useful mainly in cytological samples, data about BRAF ICC on cytological samples are missing. Methods: We performed BRAF ICC on a series of 50 FNA samples of metastatic melanoma. BRAF molecular analysis was performed on the same cytological samples or on the corresponding histological samples. Molecular analysis was considered the gold standard. Results: BRAF ICC results were adequate in 49 out of 50 (98%) cases, positive in 15 out of 50 (30%) cases and negative in 34 out of 50 (68%) of cases. Overall, BRAF ICC sensitivity, specificity, positive predictive value and negative predictive value results were 88.2%, 100%, 100% and 94.1%, respectively. The diagnostic performance of BRAF ICC results was perfect when molecular evaluation was performed on the same cytological samples. Hyperpigmentation represents the main limitation of the technique. Conclusions: BRAF ICC is a rapid, cost-effective method for detecting BRAF V600E mutation in melanoma metastases, applicable with high diagnostic performance to cytological samples. It could represent the first step to evaluate BRAF mutational status in cytological samples, mainly in poorly cellular cases.

scholarly journals Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4649
Author(s):  
İsmail Hakkı ÇAVDAR ◽  
Vahit FERYAD
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Optimization Technique ◽  
Cost Effective ◽  
Optimization Method ◽  
Long Term Memory ◽  
Successful Implementation ◽  
Efficient Design ◽  
Energy Disaggregation ◽  
Load Monitoring

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

scholarly journals An Innovative and Cost-Advantage CAD Solution for Cubitus Varus Surgical Planning in Children

2021 ◽  
Vol 11 (9) ◽  
pp. 4057
Author(s):  
Leonardo Frizziero ◽  
Gian Maria Santi ◽  
Christian Leon-Cardenas ◽  
Giampiero Donnici ◽  
Alfredo Liverani ◽  
...  
Keyword(s):  
Computer Aided Design ◽  
Low Cost ◽  
Cost Effective ◽  
Varus Deformity ◽  
Patient Specific ◽  
Cubitus Varus ◽  
Surgical Guide ◽  
Traditional Procedure ◽  
Custom Made ◽  
Patient Specific Instruments

The study of CAD (computer aided design) modeling, design and manufacturing techniques has undergone a rapid growth over the past decades. In medicine, this development mainly concerned the dental and maxillofacial sectors. Significant progress has also been made in orthopedics with pre-operative CAD simulations, printing of bone models and production of patient-specific instruments. However, the traditional procedure that formulates the surgical plan based exclusively on two-dimensional images and interventions performed without the aid of specific instruments for the patient and is currently the most used surgical technique. The production of custom-made tools for the patient, in fact, is often expensive and its use is limited to a few hospitals. The purpose of this study is to show an innovative and cost-effective procedure aimed at prototyping a custom-made surgical guide for address the cubitus varus deformity on a pediatric patient. The cutting guides were obtained through an additive manufacturing process that starts from the 3D digital model of the patient’s bone and allows to design specific models using Creo Parametric. The result is a tool that adheres perfectly to the patient’s bone and guides the surgeon during the osteotomy procedure. The low cost of the methodology described makes it worth noticing by any health institution.

scholarly journals A Multi-Objective Optimization Method for Hospital Admission Problem—A Case Study on Covid-19 Patients

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 38
Author(s):  
Amr Mohamed AbdelAziz ◽  
Louai Alarabi ◽  
Saleh Basalamah ◽  
Abdeltawab Hendawi
Keyword(s):  
Medical Care ◽  
Real Life ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Admission Process ◽  
Multi Objective ◽  
Admission Time ◽  
King Faisal Specialist Hospital ◽  
Number Of Patients ◽  
Massive Number

The wide spread of Covid-19 has led to infecting a huge number of patients, simultaneously. This resulted in a massive number of requests for medical care, at the same time. During the first wave of Covid-19, many people were not able to get admitted to appropriate hospitals because of the immense number of patients. Admitting patients to suitable hospitals can decrease the in-bed time of patients, which can lead to saving many lives. Also, optimizing the admission process can minimize the waiting time for medical care, which can save the lives of severe cases. The admission process needs to consider two main criteria: the admission time and the readiness of the hospital that will accept the patients. These two objectives convert the admission problem into a Multi-Objective Problem (MOP). Pareto Optimization (PO) is a common multi-objective optimization method that has been applied to different MOPs and showed its ability to solve them. In this paper, a PO-based algorithm is proposed to deal with admitting Covid-19 patients to hospitals. The method uses PO to vary among hospitals to choose the most suitable hospital for the patient with the least admission time. The method also considers patients with severe cases by admitting them to hospitals with the least admission time regardless of their readiness. The method has been tested over a real-life dataset that consisted of 254 patients obtained from King Faisal specialist hospital in Saudi Arabia. The method was compared with the lexicographic multi-objective optimization method regarding admission time and accuracy. The proposed method showed its superiority over the lexicographic method regarding the two criteria, which makes it a good candidate for real-life admission systems.

scholarly journals Economic Development Based on a Mathematical Model: An Optimal Solution Method for the Fuel Supply of International Road Transport Activity

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2963
Author(s):  
Melinda Timea Fülöp ◽  
Miklós Gubán ◽  
György Kovács ◽  
Mihály Avornicului
Keyword(s):  
Decision Making ◽  
Ad Hoc ◽  
Market Competition ◽  
Optimal Solution ◽  
Cost Savings ◽  
Cost Effective ◽  
Optimization Method ◽  
Optimal Decision ◽  
Environmental Damage ◽  
Fuel Cost

Due to globalization and increased market competition, forwarding companies must focus on the optimization of their international transport activities and on cost reduction. The minimization of the amount and cost of fuel results in increased competition and profitability of the companies as well as the reduction of environmental damage. Nowadays, these aspects are particularly important. This research aims to develop a new optimization method for road freight transport costs in order to reduce the fuel costs and determine optimal fueling stations and to calculate the optimal quantity of fuel to refill. The mathematical method developed in this research has two phases. In the first phase the optimal, most cost-effective fuel station is determined based on the potential fuel stations. The specific fuel prices differ per fuel station, and the stations are located at different distances from the main transport way. The method developed in this study supports drivers’ decision-making regarding whether to refuel at a farther but cheaper fuel station or at a nearer but more expensive fuel station based on the more economical choice. Thereafter, it is necessary to determine the optimal fuel volume, i.e., the exact volume required including a safe amount to cover stochastic incidents (e.g., road closures). This aspect of the optimization method supports drivers’ optimal decision-making regarding optimal fuel stations and how much fuel to obtain in order to reduce the fuel cost. Therefore, the application of this new method instead of the recently applied ad-hoc individual decision-making of the drivers results in significant fuel cost savings. A case study confirmed the efficiency of the proposed method.

Multiobjective Optimization of the Heating Process for Forging Automotive Crankshaft

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
Hong-Seok Park ◽  
Xuan-Phuong Dang
Keyword(s):  
Multiobjective Optimization ◽  
Heating System ◽  
Optimization Method ◽  
Heating Process ◽  
Temperature Deviation ◽  
Engineering Data ◽  
Optimization Of Process Parameters ◽  
Mathematical Approximation ◽  
Nondominated Sorting ◽  
Induction Heating System

This paper presents potential approaches that increase the energy efficiency of an in-line induction heating system for forging of an automotive crankshaft. Both heat loss reduction and optimization of process parameters are proposed scientifically in order to minimize the energy consumption and the temperature deviation in the workpiece. We applied the numerical multiobjective optimization method in conjunction with the design of experiment (DOE), mathematical approximation with metamodel, nondominated sorting genetic algorithm (GA), and engineering data mining. The results show that using the insulating covers reduces heat by an amount equivalent to 9% of the energy stored in the heated workpiece, and approximately 5.8% of the energy can be saved by process parameter optimization.

scholarly journals Seeking optimal groundwater pumping strategies at Pinggu District in Beijing, China

2012 ◽  
Vol 15 (2) ◽  
pp. 607-619 ◽  
Author(s):  
A. L. Yang ◽  
G. H. Huang ◽  
X. S. Qin ◽  
L. Li ◽  
W. Li
Keyword(s):  
Groundwater Management ◽  
Fuzzy Optimization ◽  
Cost Effective ◽  
Optimization Method ◽  
Chance Constrained Programming ◽  
Groundwater Pumping ◽  
Simulation And Optimization ◽  
Study Results ◽  
Pumping Rates ◽  
Beijing China

A simulation-based fuzzy optimization method (SFOM) was proposed for regional groundwater pumping management in considering uncertainties. SFOM enhanced the traditional groundwater management models by incorporating a response matrix model (RMM) into a fuzzy chance-constrained programming (FCCP) framework. RMM was used to approximate the input–output relationship between pumping actions and subsurface hydrologic responses. Due to its explicit expression, RMM could be easily embedded into an optimization model to help seek cost-effective pumping solutions. A groundwater management case in Pinggu District of Beijing, China, was used to demonstrate the method's applicability. The study results showed that the obtained system cost and pumping rates would vary significantly under different confidence levels of constraints satisfaction. The decision-makers could identify the best groundwater pumping strategy through analyzing the tradeoff between the risk of violating the related water resources conservation target and the economic benefit. Compared with traditional approaches, SFOM was particularly advantageous in linking simulation and optimization models together, and tackling uncertainties using fuzzy chance constraints.

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