Identifying Synergistic Components of Botanical Fungicide Formulations Using Interpretable Graph Neural Networks.

Botanical formulations are promising candidates for developing new biopesticides that can protect crops from pests and diseases while reducing harm to the environment. These biopesticides can be combined with permeation enhancer compounds to boost their efficacy against pests and fungal diseases. However, finding synergistic combinations of these compounds is challenging due to the large and complex chemical space. In this paper, we propose a novel deep learning method that can predict the synergy of botanical products and permeation enhancers based on in vitro assay data. Our method uses a weighted combination of component feature vectors to represent the input mixtures, which enables the model to handle a variable number of components and to interpret the contribution of each component to the synergy. We also employ an ensemble of interpretation methods to provide insights into the underlying mechanisms of synergy. We validate our method by testing the predicted synergistic combinations in wet-lab experiments and show that our method can discover novel and effective biopesticides that would otherwise be difficult to find. Our method is generalizable and applicable to other domains, where predicting mixtures of chemical compounds is important.

Evaluation of Darolutamide (ODM201) Efficiency on Androgen Receptor Mutants Reported to Date in Prostate Cancer Patients.

Resistance to drug treatments is common in prostate cancer (PCa), and the gain-of-function mutations in human androgen receptor (AR) represent one of the most dominant drivers of progression to resistance to AR pathway inhibitors (ARPI). Previously, we evaluated the in vitro response of 24 AR mutations, identified in men with castration-resistant PCa, to five AR antagonists. In the current work, we evaluated 44 additional PCa-associated AR mutants, reported in the literature, and thus expanded the study of the effect of darolutamide to a total of 68 AR mutants. Unlike other AR antagonists, we demonstrate that darolutamide exhibits consistent efficiency against all characterized gain-of-function mutations in a full-length AR. Additionally, the response of the AR mutants to clinically used bicalutamide and enzalutamide, as well as to major endogenous steroids (DHT, estradiol, progesterone and hydrocortisone), was also investigated. As genomic profiling of PCa patients becomes increasingly feasible, the developed "AR functional encyclopedia" could provide decision-makers with a tool to guide the treatment choice for PCa patients based on their AR mutation status.

Deep Learning Modeling of Androgen Receptor Responses to Prostate Cancer Therapies.

Gain-of-function mutations in human androgen receptor (AR) are among the major causes of drug resistance in prostate cancer (PCa). Identifying mutations that cause resistant phenotype is of critical importance for guiding treatment protocols, as well as for designing drugs that do not elicit adverse responses. However, experimental characterization of these mutations is time consuming and costly; thus, predictive models are needed to anticipate resistant mutations and to guide the drug discovery process. In this work, we leverage experimental data collected on 68 AR mutants, either observed in the clinic or described in the literature, to train a deep neural network (DNN) that predicts the response of these mutants to currently used and experimental anti-androgens and testosterone. We demonstrate that the use of this DNN, with general 2D descriptors, provides a more accurate prediction of the biological outcome (inhibition, activation, no-response, mixed-response) in AR mutant-drug pairs compared to other machine learning approaches. Finally, the developed approach was used to make predictions of AR mutant response to the latest AR inhibitor darolutamide, which were then validated by in-vitro experiments.

Uncovering the subtype-specific temporal order of cancer pathway dysregulation.

Cancer is driven by genetic mutations that dysregulate pathways important for proper cell function. Therefore, discovering these cancer pathways and their dysregulation order is key to understanding and treating cancer. However, the heterogeneity of mutations between different individuals makes this challenging and requires that cancer progression is studied in a subtype-specific way. To address this challenge, we provide a mathematical model, called Subtype-specific Pathway Linear Progression Model (SPM), that simultaneously captures cancer subtypes and pathways and order of dysregulation of the pathways within each subtype. Experiments with synthetic data indicate the robustness of SPM to problem specifics including noise compared to an existing method. Moreover, experimental results on glioblastoma multiforme and colorectal adenocarcinoma show the consistency of SPM's results with the existing knowledge and its superiority to an existing method in certain cases. The implementation of our method is available at https://github.com/Dalton386/SPM.

Androgen receptor plasticity and its implications for prostate cancer therapy.

Acquired resistance to a drug treatment is a common problem across many cancers including prostate cancer (PCa) - one of the major factors for male mortality. The androgen receptor (AR) continues to be the main therapeutic PCa target and despite the success of modern targeted therapies such as enzalutamide, resistance to these drugs eventually develops. The AR has found many ways to adapt to treatments including overexpression and production of functional, constitutively active splice variants. However, of particular importance are point mutations in the ligand binding domain of the protein that convert anti-androgens into potent AR agonists. This mechanism appears to be especially prevalent with the AR in spite of some distant similarities to other hormone nuclear receptors. Despite the AR being one of the most studied and attended targets in cancer, those gain-of-function mutations in the receptor remain a significant challenge for the development of PCa therapies. This drives the need to fully characterize such mutations and to consistently screen PCa patients for their occurrence to prevent adverse reactions to anti-androgen drugs. Novel treatments should also be developed to overcome this resistance mechanism and more attention should be given to the possibility of similar occurrences in other cancers.