Browsing by Author "Pahikkala, Tapio"

Now showing 1 - 4 of 4
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    Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors
    (2017-08-01) Cichonska, Anna; Ravikumar, Balaguru; Parri, Elina; Timonen, Sanna; Pahikkala, Tapio; Airola, Antti; Wennerberg, Krister; Rousu, Juho; Aittokallio, Tero; Department of Computer Science; Professorship Rousu Juho; Helsinki Institute for Information Technology (HIIT); University of Helsinki; University of Turku
    Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel-based modeling approach offers practical benefits for probing novel insights into the mode of action of investigational compounds, and for the identification of new target selectivities for drug repurposing applications.
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    Learning with multiple pairwise kernels for drug bioactivity prediction
    (2018-07-01) Cichonska, Anna; Pahikkala, Tapio; Szedmak, Sandor; Julkunen, Heli; Airola, Antti; Heinonen, Markus; Aittokallio, Tero; Rousu, Juho; Department of Computer Science; Professorship Rousu Juho; Helsinki Institute for Information Technology (HIIT); Professorship Lähdesmäki Harri; Centre of Excellence in Molecular Systems Immunology and Physiology Research Group, SyMMys; University of Turku; Aalto University
    Motivation: Many inference problems in bioinformatics, including drug bioactivity prediction, can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel-based approaches have emerged as powerful tools for solving problems of that kind, and especially multiple kernel learning (MKL) offers promising benefits as it enables integrating various types of complex biomedical information sources in the form of kernels, along with learning their importance for the prediction task. However, the immense size of pairwise kernel spaces remains a major bottleneck, making the existing MKL algorithms computationally infeasible even for small number of input pairs. Results: We introduce pairwiseMKL, the first method for time- and memory-efficient learning with multiple pairwise kernels. pairwiseMKL first determines the mixture weights of the input pairwise kernels, and then learns the pairwise prediction function. Both steps are performed efficiently without explicit computation of the massive pairwise matrices, therefore making the method applicable to solving large pairwise learning problems. We demonstrate the performance of pairwiseMKL in two related tasks of quantitative drug bioactivity prediction using up to 167 995 bioactivity measurements and 3120 pairwise kernels: (i) prediction of anticancer efficacy of drug compounds across a large panel of cancer cell lines; and (ii) prediction of target profiles of anticancer compounds across their kinome-wide target spaces. We show that pairwiseMKL provides accurate predictions using sparse solutions in terms of selected kernels, and therefore it automatically identifies also data sources relevant for the prediction problem.
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    Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects
    (Nature Publishing Group, 2020-12-01) Julkunen, Heli; Cichonska, Anna; Gautam, Prson; Szedmak, Sandor; Douat, Jane; Pahikkala, Tapio; Aittokallio, Tero; Rousu, Juho; Department of Computer Science; Professorship Rousu Juho; Helsinki Institute for Information Technology (HIIT); Computer Science - Computational Life Sciences (CSLife); University of Helsinki; Department of Computer Science; University of Turku; Aalto University
    We 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.
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    Modeling drug combination effects via latent tensor reconstruction
    (OXFORD UNIV PRESS INC, 2021-07-01) Wang, Tianduanyi; Szedmak, Sandor; Wang, Haishan; Aittokallio, Tero; Pahikkala, Tapio; Cichonska, Anna; Rousu, Juho; Department of Computer Science; Helsinki Institute for Information Technology (HIIT); Professorship Rousu Juho; Computer Science Professors; Computer Science - Large-scale Computing and Data Analysis (LSCA); Computer Science - Artificial Intelligence and Machine Learning (AIML); Computer Science - Computational Life Sciences (CSLife); Department of Computer Science; Aalto University; University of Turku
    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-A nd cost-efficient means to aid this process by prioritizing 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 modeling of drug combination effects. Results: We introduce comboLTR, highly time-efficient method for learning complex, non-linear 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.