scholarly journals An Optimized Modeling Algorithm for Breast Cancer Drug Candidates Based on NSGAII

2021 ◽  
Vol 2 (3) ◽  
pp. 50-57
Author(s):  
Chenyao Fan ◽  
Huawei Mei
Keyword(s):  
Breast Cancer ◽  
Biological Activity ◽  
Malignant Tumors ◽  
Molecular Descriptor ◽  
Classification Model ◽  
Cancer Drug ◽  
Residual Network ◽  
Drug Candidates ◽  
Starting Point ◽  
The Impact

Breast cancer is one of the most common malignant tumors in women. It seriously threatens the safety of women worldwide. It is an important and urgent task to research and develop anti-breast cancer drugs and improve the therapeutic effect of breast cancer. Taking the actual sample data as the main starting point, firstly, the prediction model of pIC50 is established by ResNet residual network and neural network (NN) to judge the biological activity. Then the classification model of ADMET property is established by ResNet residual network and LightGBM, and the model fusion is realized by Choquet fuzzy integral. Finally, the NSGAII multi-objective optimization algorithm is used to determine the range of values that each molecular descriptor obtains in the range of good biological activity, and ultimately to optimize the modeling of anti-breast cancer drug candidates. The experimental results show that the algorithm improves the prediction accuracy of biological activity, realizes the efficient and accurate classification of ADMET properties, and accurately describes the impact of molecular descriptors on biological activity.

scholarly journals Antiangiogenic therapy for breast cancer with triple negative phenotype

2021 ◽  
Vol 23 (1) ◽  
pp. 88-92
Author(s):  
Inna P. Ganshina ◽  
Kristina A. Ivanova ◽  
Olga O. Gordeeva ◽  
Aleksandr V. Arkhipov ◽  
Liudmila G. Zhukova
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Malignant Tumors ◽  
Antiangiogenic Therapy ◽  
Treatment Strategies ◽  
Her 2 ◽  
Triple Negative Phenotype ◽  
The Impact ◽  
Negative Phenotype

Triple-negative breast cancer is 1024% of all cases of breast cancer and is characterized by the absence of estrogen, progesterone, and HER-2 receptors in the tumor. The therapy of this illness is a difficult clinical case. In contrast to hormone-positive and HER-2-positive phenotypes, in which we successfully use targeted drugs (antiestrogens and anti-HER-2 drugs), for triple-negative breast cancer we have not had such targets for a long time. Thus, despite the impressive results of immunotherapy of triple-negative breast cancer, there remains a fairly large group of patients with negative PD-L1 status, for whom it is necessary to develop other treatment strategies. One of the approaches in the treatment of malignant tumors includes not the impact on tumor cells, but the process of angiogenesis. Antiangiogenic drugs have positively proven themselves in the treatment of a large number of malignant tumors but are underestimated for breast cancer (including triple-negative phenotype). The use of bevacizumab in combinations with cytostatic drugs in breast cancer therapy (including triple-negative breast cancer) has been studied in a large number of clinical trials but was undeservedly forgotten in some countries due to the revoked FDA registration. This review presents the role of bevacizumab in the treatment of patients with triple-negative breast cancer and suggests the conditions when the administration of this drug is justified and leads to better results.

Ferrocene-Mediated Proton-Coupled Electron Transfer in a Series of Ferrocifen-Type Breast-Cancer Drug Candidates

2006 ◽  
Vol 118 (2) ◽  
pp. 291-296 ◽  
Author(s):  
Elizabeth Hillard ◽  
Anne Vessières ◽  
Laurent Thouin ◽  
Gérard Jaouen ◽  
Christian Amatore
Keyword(s):  
Breast Cancer ◽  
Electron Transfer ◽  
Cancer Drug ◽  
Drug Candidates ◽  
Proton Coupled Electron Transfer

Ferrocene-Mediated Proton-Coupled Electron Transfer in a Series of Ferrocifen-Type Breast-Cancer Drug Candidates

2006 ◽  
Vol 45 (2) ◽  
pp. 285-290 ◽  
Author(s):  
Elizabeth Hillard ◽  
Anne Vessières ◽  
Laurent Thouin ◽  
Gérard Jaouen ◽  
Christian Amatore
Keyword(s):  
Breast Cancer ◽  
Electron Transfer ◽  
Cancer Drug ◽  
Drug Candidates ◽  
Proton Coupled Electron Transfer

scholarly journals Impact of variants in ATP-binding cassette transporters on breast cancer treatment

2020 ◽  
Vol 21 (18) ◽  
pp. 1299-1310
Author(s):  
Qingyang Xiao ◽  
Yitian Zhou ◽  
Volker M Lauschke
Keyword(s):  
Breast Cancer ◽  
Rare Variants ◽  
Therapy Response ◽  
Cancer Drug ◽  
Atp Binding ◽  
Breast Cancer Patients ◽  
Cancer Drug Resistance ◽  
The Impact ◽  
Substantial Interest ◽  
Atp Binding Cassette

There has been substantial interest in the impact of ATP-binding cassette (ABC) transporter variability on breast cancer drug resistance. Here, we provide a systematic review of ABC variants in breast cancer therapy. Notably, most studies used small heterogeneous cohorts and their identified associations lack statistical stringency, replication and mechanistic support. We conclude that commonly studied ABC polymorphisms are not suitable to accurately predict therapy response or toxicity in breast cancer patients and cannot guide treatment decisions. However, recent research shows that ABC transporters harbor a plethora of rare variants with individually small effect sizes, and we argue that a shift in strategy from target variant interrogation to comprehensive profiling might hold promise to drastically improve the predictive power of outcome models.

scholarly journals Anti-breast cancer drug screening based on Neural Networks and QSAR model

2021 ◽  
Vol 12 (6) ◽  
pp. 401-406
Author(s):  
Bin Zhao ◽  
Renxiong Xie ◽  
Xia Jiang
Keyword(s):  
Breast Cancer ◽  
Biological Activity ◽  
Back Propagation ◽  
Qsar Model ◽  
Estrogen Receptor Α ◽  
Back Propagation Neural Network ◽  
Cancer Drug ◽  
Complex Process ◽  
Average Accuracy ◽  
Relationship Of

Breast cancer is one of the most lethal cancers, estrogen receptor α Subtype (ERα) is an important target. The compounds that able to fight ERα active may be candidates for treatment of breast cancer. The drug discovery process is a very large and complex process that often requires one selected from a large number of compounds. This paper considers the independence, coupling, and relevance of bioactivity descriptors, selects the 15 most potentially valuable bioactivity descriptors from 729 bioactivity descriptors. An optimized back propagation neural network is used for ERα, The pharmacokinetics and safety of 15 selected bioactivity descriptors were verified by gradient lifting algorithm. The results showed that these 15 biological activity descriptors could not only fit well with the nonlinear relationship of ERα activity can also accurately predict its pharmacokinetic characteristics and safety, with an average accuracy of 89.92~94.80%. Therefore, these biological activity descriptors have great medical research value.

scholarly journals Retrospective cohort analysis of prescription patterns of cancer medications during periods of drug stockouts in Botswana

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e049574
Author(s):  
Sonya Davey ◽  
Surbhi Grover ◽  
Warren B Bilker ◽  
Dipho I Setlhako ◽  
Tlotlo B Ralefala ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Retrospective Cohort ◽  
Cohort Analysis ◽  
Kaposi Sarcoma ◽  
Sub Saharan Africa ◽  
Secondary Outcome ◽  
Cancer Drug ◽  
Breast Cancer Patients ◽  
Prescription Patterns ◽  
The Impact

ObjectiveCancer drug stockouts occur at high frequencies globally, however, their effects on treatment are understudied in sub-Saharan Africa (SSA). We aimed to determine whether causes of suboptimal cancer treatment prescriptions differed between periods of stockout and full treatment supply.DesignA retrospective cohort study of systemic therapy prescriptions for patients diagnosed with the twelve most common solid tumour cancers treated in 2016.SettingPrincess Marina Hospital in Gaborone, Botswana.ParticipantsPatients in the retrospective cohort who experienced any suboptimal treatment events, defined as ≥7 days delay or switch from guideline-concordant initiated therapy.Primary and secondary outcome measuresFrequency of delays and patterns of prescription changes for specific regimens and cancer types.Results167/378 patients contributed to 320 suboptimal events (115 therapy switches, 167 delays and 38 events with both), over 1452 total chemotherapy cycles received. Events during stockout were 43% delays, 43% switches and 14% both during stockout periods and 67.2% delays, 24.4% switches and 8.4% both during non-stockout periods (p<0.001). Majority of switches involved de-escalation of initially prescribed guideline-recommended regimens in patients with breast cancer, Kaposi sarcoma and patients with colorectal cancer, which occurred more frequently during periods of drug stockouts. Among patients with breast cancer, substitution of docetaxel for paclitaxel event occurred exclusively during paclitaxel drug stockout. Delays of ≥7 days events were most frequent in breast cancer patients receiving paclitaxel during stockout, and combination doxorubicin and cyclophosphamide even during periods of non-stockout.ConclusionsThe aetiology of suboptimal events differed during stockout and non-stockout periods. Prescription patterns that involved de-escalation of initiated therapy and substitution of paclitaxel with docetaxel occurred frequently during periods of drug stockout. Further research needs to be conducted to understand the impact of stockout on survival and barriers to maintaining essential cancer medicines supplies in SSA, and the factors driving frequent delays in therapy delivery.

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