scholarly journals Logical modeling: Combining manual curation and automated parameterization to predict drug synergies

2021 ◽  
Author(s):  
Åsmund Flobak ◽  
John Zobolas ◽  
Miguel Vazquez ◽  
Tonje Strømmen Steigedal ◽  
Liv Thommesen ◽  
...  
Keyword(s):  
Predictive Performance ◽  
Drug Combinations ◽  
Great Promise ◽  
Small Subset ◽  
Calibration Data ◽  
Model Optimization ◽  
Manual Curation ◽  
Logical Modeling ◽  
Model Ensembles ◽  
Correct Topology

Treatment with drug combinations carries great promise for personalized therapy. We have previously shown that drug synergies targeting cancer can manually be identified based on a logical framework. We now demonstrate how automated adjustments of model topology and logic equations can greatly reduce the workload traditionally associated with logical model optimization. Our methodology allows the exploration of larger model ensembles that all obey a set of observations. We benchmark synergy predictions against a dataset of 153 targeted drug combinations. We show that well-performing manual models faithfully represent measured biomarker data and that their performance can be outmatched by automated parameterization using a genetic algorithm. The predictive performance of a curated model is strongly affected by simulated curation errors, while data-guided deletion of a small subset of edges can improve prediction quality. With correct topology we find some tolerance to simulated errors in the biomarker calibration data. With our framework we predict the synergy of joint inhibition of PI3K and TAK1, and further substantiate this prediction with observation in cancer cell cultures and in xenograft experiments.

scholarly journals Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Heli Julkunen ◽  
Anna Cichonska ◽  
Prson Gautam ◽  
Sandor Szedmak ◽  
Jane Douat ◽  
...  
Keyword(s):  
Effective Means ◽  
Cancer Cell Line ◽  
Predictive Performance ◽  
Drug Combinations ◽  
Precision Oncology ◽  
Learning Framework ◽  
Anaplastic Lymphoma ◽  
Higher Order Tensors ◽  
Using Data ◽  
Context Specific

AbstractWe present comboFM, a machine learning framework for predicting the responses of drug combinations in pre-clinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell context-specific drug interactions through higher-order tensors, and efficiently learns latent factors of the tensor using powerful factorization machines. The approach enables comboFM to leverage information from previous experiments performed on similar drugs and cells when predicting responses of new combinations in so far untested cells; thereby, it achieves highly accurate predictions despite sparsely populated data tensors. We demonstrate high predictive performance of comboFM in various prediction scenarios using data from cancer cell line pharmacogenomic screens. Subsequent experimental validation of a set of previously untested drug combinations further supports the practical and robust applicability of comboFM. For instance, we confirm a novel synergy between anaplastic lymphoma kinase (ALK) inhibitor crizotinib and proteasome inhibitor bortezomib in lymphoma cells. Overall, our results demonstrate that comboFM provides an effective means for systematic pre-screening of drug combinations to support precision oncology applications.

scholarly journals Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold-hopping capability

2019 ◽  
Author(s):  
Oliver Laufkötter ◽  
Noé Sturm ◽  
Jürgen Bajorath ◽  
Ola Engkvist ◽  
Hongming Chen
Keyword(s):  
High Throughput Screening ◽  
Synergistic Effects ◽  
Predictive Performance ◽  
Small Subset ◽  
Scaffold Hopping ◽  
Activity Prediction ◽  
Importance Analysis ◽  
Supporting Effect ◽  
Overall Performance ◽  
The Individual

This study aims at improving upon existing activity predictions methods by augmenting chemical structure fingerprints with bio-activity based fingerprints derived from high-throughput screening (HTS) data (HTSFPs). The HTSFPs were generated from HTS data obtained from PubChem and combined with an ECFP4 structural fingerprint. The combined experimental and structural fingerprint (CESFP) was benchmarked against the individual ECFP4 and HTSFP fingerprints. Results showed that the CESFP has improved predictive performance as well as scaffold hopping capability. The CESFP identified unique compounds compared to both the ECFP4 and the HTSFP fingerprint indicating synergistic effects between the two fingerprints. A feature importance analysis showed that a small subset of the HTSFP features contribute most to the overall performance of the CESFP. This combined approach allows for activity prediction of compounds with only sparse HTSFPs due to the supporting effect from the structural fingerprint.

scholarly journals Modeling drug combination effects via latent tensor reconstruction

2021 ◽  
Author(s):  
Tianduanyi Wang ◽  
Sandor Szedmak ◽  
Haishan Wang ◽  
Tero Aittokallio ◽  
Tapio Pahikkala ◽  
...  
Keyword(s):  
Drug Combination ◽  
Polynomial Regression ◽  
Predictive Performance ◽  
Predictive Modelling ◽  
Drug Combinations ◽  
Multiple Drug ◽  
Interaction Patterns ◽  
Combination Effects ◽  
Drug Doses ◽  
Cost Efficient

Motivation: Combination therapies have emerged as a powerful treatment modality to overcome drug resistance and improve treatment efficacy. However, the number of possible drug combinations increases very rapidly with the number of individual drugs in consideration which makes the comprehensive experimental screening infeasible in practice. Machine learning models offer time- and cost-efficient means to aid this process by prioritising the most effective drug combinations for further pre-clinical and clinical validation. However, the complexity of the underlying interaction patterns across multiple drug doses and in different cellular contexts poses challenges to the predictive modelling of drug combination effects. Results: We introduce comboLTR, highly time-efficient method for learning complex, nonlinear target functions for describing the responses of therapeutic agent combinations in various doses and cancer cell-contexts. The method is based on a polynomial regression via powerful latent tensor reconstruction. It uses a combination of recommender system-style features indexing the data tensor of response values in different contexts, and chemical and multi-omics features as inputs. We demonstrate that comboLTR outperforms state-of-the-art methods in terms of predictive performance and running time, and produces highly accurate results even in the challenging and practical inference scenario where full dose-response matrices are predicted for completely new drug combinations with no available combination and monotherapy response measurements in any training cell line.

scholarly journals Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold-hopping capability

2019 ◽  
Author(s):  
Oliver Laufkötter ◽  
Noé Sturm ◽  
Jürgen Bajorath ◽  
Ola Engkvist ◽  
Hongming Chen
Keyword(s):  
High Throughput Screening ◽  
Synergistic Effects ◽  
Predictive Performance ◽  
Small Subset ◽  
Scaffold Hopping ◽  
Activity Prediction ◽  
Importance Analysis ◽  
Supporting Effect ◽  
Overall Performance ◽  
The Individual

This study aims at improving upon existing activity predictions methods by augmenting chemical structure fingerprints with bio-activity based fingerprints derived from high-throughput screening (HTS) data (HTSFPs). The HTSFPs were generated from HTS data obtained from PubChem and combined with an ECFP4 structural fingerprint. The combined experimental and structural fingerprint (CESFP) was benchmarked against the individual ECFP4 and HTSFP fingerprints. Results showed that the CESFP has improved predictive performance as well as scaffold hopping capability. The CESFP identified unique compounds compared to both the ECFP4 and the HTSFP fingerprint indicating synergistic effects between the two fingerprints. A feature importance analysis showed that a small subset of the HTSFP features contribute most to the overall performance of the CESFP. This combined approach allows for activity prediction of compounds with only sparse HTSFPs due to the supporting effect from the structural fingerprint.

scholarly journals IMPROVING PERFORMANCE OF INDUCTIVE MODELS THROUGH AN ALGORITHM AND SAMPLE COMBINATION STRATEGY

2001 ◽  
Vol 10 (04) ◽  
pp. 555-572
Author(s):  
HALEH VAFAIE ◽  
DEAN ABBOTT ◽  
MARK HUTCHINS ◽  
I. PHILIP MATKOVSKY
Keyword(s):  
Data Mining ◽  
Negative Impact ◽  
Predictive Performance ◽  
Training Sample ◽  
Model Combination ◽  
Combination Strategy ◽  
Combination Methods ◽  
Sample Data ◽  
Model Ensembles ◽  
Mining Model

Multiple approaches have been developed for improving predictive performance of a system by creating and combining various learned models. There are two main approaches to creating model ensembles. This first is to create a set of learned models by applying an algorithm repeatedly to different training sample data, the second applies various learning algorithms to the same sample data. The predictions of the models are then combined accordings to a voting scheme. This paper presents a method for combining models that were developed using numerous samples, modeling algorithms, and modelers and compares it with the alternate approaches. The presented results are based on findings from an ongoing operational data mining initiative with respect to selecting a model set that is best able to meet defined goals from among trained models. The operational goals to be attained in this initiative are to deploy data mining model(s) that maximizes specificity with minimal negative impact to sensitivity. The results of the model combination methods are evaluated with respect to sensitivity and false alarm rates and are then compared against other approaches.

scholarly journals A Random Walk with Restart Model Based on Common Neighbors for Predicting the Clinical Drug Combinations on Coronary Heart Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yushi Che ◽  
Wei Cheng ◽  
Yiqiao Wang ◽  
Dong Chen
Keyword(s):  
Random Walk ◽  
High Throughput Screening ◽  
Fundamental Problem ◽  
Predictive Performance ◽  
Cost Effective ◽  
Drug Combinations ◽  
Random Walk With Restart ◽  
Random Walk Algorithm ◽  
Improved Model ◽  
Walk Algorithm

As the approaching of the clinical big data era, the prediction of whether drugs can be used in combination in clinical practice is a fundamental problem in the analysis of medical data. Compared with high-throughput screening, it is more cost-effective to treat this problem as a link prediction problem and predict by algorithms. Inspired by the rule of combined clinical medication, a new computational model is proposed. The drug-drug combination was predicted by combining the number of adjacent complete subgraphs shared by the two points with the restart random walk algorithm. The model is based on the semisupervised random walk algorithm, and the same neighborhood is used to improve the random walk with restart (CN-RWR). The algorithm can effectively improve the prediction performance and assign a score to any combination of drugs. To fairly compare the predictive performance of the improved model with that of the random walk with restart model (RWR), a cross-validation of the two models on the same drug data was performed. The AUROC of CN-RWR and RWR under the LOOCV validation framework is 0.9741 and 0.9586, respectively, and the improved model results are more reliable. In addition, the top 3 predictive drug combinations have been approved by the public. The new model is expected that this model can be extended to predict the use of combination drugs for other diseases to find combinations of drugs with potential clinical benefits.

Artificial Intelligence-Driven Designer Drug Combinations: From Drug Development to Personalized Medicine

2018 ◽  
Vol 24 (1) ◽  
pp. 124-125
Author(s):  
Masturah Bte Mohd Abdul Rashid ◽  
Edward Kai-Hua Chow
Keyword(s):  
Artificial Intelligence ◽  
Personalized Medicine ◽  
Translational Medicine ◽  
Molecular Mechanisms ◽  
Disease Model ◽  
Drug Combinations ◽  
Designer Drug ◽  
Great Promise ◽  
Specific Patient ◽  
And Personalized Medicine

Artificial intelligence holds great promise in transforming how drugs are designed and patients are treated. In a study recently published in Science Translational Medicine, a unique artificial intelligence platform makes efficient use of small experimental datasets to design new drug combinations as well as identify the best drug combinations for specific patient samples. This quadratic phenotypic optimization platform (QPOP) does not rely on previous assumptions of molecular mechanisms of disease, but rather uses system-specific experimental data to determine the best drug combinations for a specific disease model or a patient sample. In this commentary, we explore how QPOP was applied toward multiple myeloma in the study. We also discuss how this study demonstrates the potential for applications of QPOP toward improving therapeutic regimen design and personalized medicine.

scholarly journals Assumption-Free Assessment of Corpus Callosum Shape: Benchmarking and Application

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Erin I. Walsh ◽  
Marnie E. Shaw ◽  
Daniela A. Espinoza Oyarce ◽  
Mark Fraser ◽  
Nicolas Cherbuin
Keyword(s):  
Corpus Callosum ◽  
Shape Analysis ◽  
Principal Component ◽  
Predictive Performance ◽  
Shape Space ◽  
Great Promise ◽  
Mixed Effect ◽  
Global Shape ◽  
Dimensional Shape ◽  
Intuitive Method

Shape analysis provides a unique insight into biological processes. This paper evaluates the properties, performance, and utility of elliptical Fourier (eFourier) analysis to operationalise global shape, focussing on the human corpus callosum. 8000 simulated corpus callosum contours were generated, systematically varying in terms of global shape (midbody arch, splenium size), local complexity (surface smoothness), and nonshape characteristics (e.g., rotation). 2088 real corpus callosum contours were manually traced from the PATH study. Performance of eFourier was benchmarked in terms of its capacity to capture and then reconstruct shape and systematically operationalise that shape via principal components analysis. We also compared the predictive performance of corpus callosum volume, position in Procrustes-aligned Landmark tangent space, and position in eFourier n-dimensional shape space in relation to the Symbol Digit Modalities Test. Jaccard index for original vs. reconstructed from eFourier shapes was excellent (M=0.98). The combination of eFourier and PCA performed particularly well in reconstructing known n-dimensional shape space but was disrupted by the inclusion of local shape manipulations. For the case study, volume, eFourier, and landmark measures were all correlated. Mixed effect model results indicated all methods detected similar features, but eFourier estimates were most predictive, and of the two shape operationalization techniques had the least error and better model fit. Elliptical Fourier analysis, particularly in combination with principal component analysis, is a powerful, assumption-free and intuitive method of quantifying global shape of the corpus callosum and shows great promise for shape analysis in neuroimaging more broadly.

scholarly journals DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal

2021 ◽  
Author(s):  
Shuyu Zheng ◽  
Jehad Aldahdooh ◽  
Tolou Shadbahr ◽  
Yinyin Wang ◽  
Dalal Aldahdooh ◽  
...  
Keyword(s):  
Drug Combination ◽  
Drug Combinations ◽  
Data Repository ◽  
Graphical Interface ◽  
Network Modelling ◽  
Web Based ◽  
Manual Curation ◽  
Cancer Sample ◽  
Sensitivity Data ◽  
User Friendly

Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here we report significant updates of DrugComb, including: 1) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; 2) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; 3) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample; and 4) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.

scholarly journals Machine learning methods, databases and tools for drug combination prediction

2021 ◽  
Author(s):  
Lianlian Wu ◽  
Yuqi Wen ◽  
Dongjin Leng ◽  
Qinglong Zhang ◽  
Chong Dai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Combination ◽  
Predictive Performance ◽  
Search Space ◽  
Drug Combinations ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
High Throughput Screens ◽  
Novel Drug ◽  
Future Work

Abstract Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.

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