The application of support vector regression for prediction of the antiallodynic effect of drug combinations in the mouse model of streptozocin-induced diabetic neuropathy

MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect

Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships between drugs remains a highly challenging task. This paper proposes a novel deep learning model MMFSyn based on multimodal drug data combined with cell line features. Firstly, to ensure the full expression of drug molecular features, multiple modalities of drugs, including Morgan fingerprints, atom sequences, molecular diagrams, and atomic point cloud data, are extracted using SMILES. Secondly, for different modal data, a Bi-LSTM, gMLP, multi-head attention mechanism, and multi-scale GCNs are comprehensively applied to extract the drug feature. Then, it selects appropriate omics features from gene expression and mutation omics data of cancer cell lines to construct cancer cell line features. Finally, these features are combined to predict the synergistic anti-cancer drug combination effect. The experimental results verify that MMFSyn has significant advantages in performance compared to other popular methods, with a root mean square error of 13.33 and a Pearson correlation coefficient of 0.81, which indicates that MMFSyn can better capture the complex relationship between multimodal drug combinations and omics data, thereby improving the synergistic drug combination prediction.

AttenSyn: An Attention-Based Deep Graph Neural Network for Anticancer Synergistic Drug Combination Prediction

Identifying synergistic drug combinations is fundamentally important to treat a variety of complex diseases while avoiding severe adverse drug-drug interactions. Although several computational methods have been proposed, they highly rely on handcrafted feature engineering and cannot learn better interactive information between drug pairs, easily resulting in relatively low performance. Recently, deep-learning methods, especially graph neural networks, have been widely developed in this area and demonstrated their ability to address complex biological problems. In this study, we proposed AttenSyn, an attention-based deep graph neural network for accurately predicting synergistic drug combinations. In particular, we adopted a graph neural network module to extract high-latent features based on the molecular graphs only and exploited the attention-based pooling module to learn interactive information between drug pairs to strengthen the representations of drug pairs. Comparative results on the benchmark datasets demonstrated that our AttenSyn performs better than the state-of-the-art methods in the prediction of anticancer synergistic drug combinations. Additionally, to provide good interpretability of our model, we explored and visualized some crucial substructures in drugs through attention mechanisms. Furthermore, we also verified the effectiveness of our proposed AttenSyn on two cell lines by visualizing the features of drug combinations learnt from our model, exhibiting satisfactory generalization ability.

MPFFPSDC: A multi-pooling feature fusion model for predicting synergistic drug combinations

Drug combination therapies are common practice in the treatment of cancer, but not all combinations result in synergy. As traditional screening approaches are restricted in their ability to uncover synergistic drug combinations, computer-aided medicine is becoming a increasingly prevalent in this field. In this work, a predictive model of potential interactions between drugs named MPFFPSDC is presented, which can maintain the symmetry of drug inputs and eliminate inconsistencies in predictive results caused by different drug inputting sequences or positions. The experimental results show that MPFFPSDC outperforms comparative models in major performance indicators and exhibits better generalization for independent data. Furthermore, the case study demonstrates that our model can capture molecular substructures that contribute to the synergistic effect of two drugs. These results indicate that MPFFPSDC not only offers strong predictive performance, but also has good model interpretability that may provide new insights for the study of drug interaction mechanisms and the development of new drugs.

DSN-DDI: an accurate and generalized framework for drug-drug interaction prediction by dual-view representation learning

Drug-drug interaction (DDI) prediction identifies interactions of drug combinations in which the adverse side effects caused by the physicochemical incompatibility have attracted much attention. Previous studies usually model drug information from single or dual views of the whole drug molecules but ignore the detailed interactions among atoms, which leads to incomplete and noisy information and limits the accuracy of DDI prediction. In this work, we propose a novel dual-view drug representation learning network for DDI prediction ('DSN-DDI'), which employs local and global representation learning modules iteratively and learns drug substructures from the single drug ('intra-view') and the drug pair ('inter-view') simultaneously. Comprehensive evaluations demonstrate that DSN-DDI significantly improved performance on DDI prediction for the existing drugs by achieving a relatively improved accuracy of 13.01% and an over 99% accuracy under the transductive setting. More importantly, DSN-DDI achieves a relatively improved accuracy of 7.07% to unseen drugs and shows the usefulness for real-world DDI applications. Finally, DSN-DDI exhibits good transferability on synergistic drug combination prediction and thus can serve as a generalized framework in the drug discovery field.

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations

MOTIVATION

Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefore, computational screening has become an important way to prioritize drug combinations. Graph neural network has recently shown remarkable performance in the prediction of compound-protein interactions, but it has not been applied to the screening of drug combinations.

RESULTS

In this paper, we proposed a deep learning model based on graph neural network and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The feature embeddings of drug molecule structure and gene expression profiles were taken as input to multilayer feedforward neural network to identify the synergistic drug combinations. We compared DeepDDS (Deep Learning for Drug-Drug Synergy prediction) with classical machine learning methods and other deep learning-based methods on benchmark data set, and the leave-one-out experimental results showed that DeepDDS achieved better performance than competitive methods. Also, on an independent test set released by well-known pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive methods by more than 16% predictive precision. Furthermore, we explored the interpretability of the graph attention network and found the correlation matrix of atomic features revealed important chemical substructures of drugs. We believed that DeepDDS is an effective tool that prioritized synergistic drug combinations for further wet-lab experiment validation.

AVAILABILITY AND IMPLEMENTATION

Source code and data are available at https://github.com/Sinwang404/DeepDDS/tree/master.

Effect of pregabalin on contextual memory deficits and inflammatory state-related protein expression in streptozotocin-induced diabetic mice

Diabetes mellitus is a metabolic disease characterized by hyperglycemia due to defects in insulin secretion or its action. Complications from long-term diabetes consist of numerous biochemical, molecular, and functional tissue alterations, including inflammation, oxidative stress, and neuropathic pain. There is also a link between diabetes mellitus and vascular dementia or Alzheimer’s disease. Hence, it is important to treat diabetic complications using drugs which do not aggravate symptoms induced by the disease itself. Pregabalin is widely used for the treatment of diabetic neuropathic pain, but little is known about its impact on cognition or inflammation-related proteins in diabetic patients. Thus, this study aimed to evaluate the effect of intraperitoneal (ip) pregabalin on contextual memory and the expression of inflammatory state-related proteins in the brains of diabetic, streptozotocin (STZ)-treated mice. STZ (200 mg/kg, ip) was used to induce diabetes mellitus. To assess the impact of pregabalin (10 mg/kg) on contextual memory, a passive avoidance task was applied. Locomotor and exploratory activities in pregabalin-treated diabetic mice were assessed by using activity cages. Using Western blot analysis, the expression of cyclooxygenase-2 (COX-2), cytosolic prostaglandin E synthase (cPGES), nuclear factor (erythroid-derived 2)-like 2 (Nrf2), nuclear factor-ĸB (NF-ĸB) p50 and p65, aryl hydrocarbon receptor (AhR), as well as glucose transporter type-4 (GLUT4) was assessed in mouse brains after pregabalin treatment. Pregabalin did not aggravate STZ-induced learning deficits in vivo or influence animals’ locomotor activity. We observed significantly lower expression of COX-2, cPGES, and NF-κB p50 subunit, and higher expression of AhR and Nrf2 in the brains of pregabalin-treated mice in comparison to STZ-treated controls, which suggested immunomodulatory and anti-inflammatory effects of pregabalin. Antioxidant properties of pregabalin in the brains of diabetic animals were also demonstrated. Pregabalin does not potentiate STZ-induced cognitive decline, and it has antioxidant, immunomodulatory, and anti-inflammatory properties in mice. These results confirm the validity of its use in diabetic patients.

Graphical abstract

Random Forest Segregation of Drug Responses May Define Regions of Biological Significance

The ability to assess brain responses in unsupervised manner based on fMRI measure has remained a challenge. Here we have applied the Random Forest (RF) method to detect differences in the pharmacological MRI (phMRI) response in rats to treatment with an analgesic drug (buprenorphine) as compared to control (saline). Three groups of animals were studied: two groups treated with different doses of the opioid buprenorphine, low (LD), and high dose (HD), and one receiving saline. PhMRI responses were evaluated in 45 brain regions and RF analysis was applied to allocate rats to the individual treatment groups. RF analysis was able to identify drug effects based on differential phMRI responses in the hippocampus, amygdala, nucleus accumbens, superior colliculus, and the lateral and posterior thalamus for drug vs. saline. These structures have high levels of mu opioid receptors. In addition these regions are involved in aversive signaling, which is inhibited by mu opioids. The results demonstrate that buprenorphine mediated phMRI responses comprise characteristic features that allow a supervised differentiation from placebo treated rats as well as the proper allocation to the respective drug dose group using the RF method, a method that has been successfully applied in clinical studies.

3-[4-(3-Trifluoromethyl-phenyl)-piperazin-1-yl]-dihydrofuran-2-one and pregabalin attenuate tactile allodynia in the mouse model of chronic constriction injury

PURPOSE

There is a strong medical demand to search for novel, more efficacious and safer than available, analgesics for the treatment of neuropathic pain. This study investigated antinociceptive activity of intraperitoneally administered 3-[4-(3-trifluoromethyl-phenyl)-piperazin-1-yl]-dihydrofuran-2-one (LPP1) and pregabalin in the chronic constriction injury (CCI) model of neuropathic pain in mice and evaluated these drugs' influence on motor coordination. In addition, microscopic examinations of the sciatic nerve were performed to assess, if a surgical method or drug treatment caused changes in the structure of this nerve. Moreover, the alterations of nerve growth factor (NGF) content after drug treatment were assessed.

METHODS

Antiallodynic and antihyperalgesic activities of LPP1 and pregabalin were assessed in the von Frey and hot plate tests. Motor-impairing properties were evaluated in the rotarod test. Microscopic examinations of the sciatic nerve were performed using electron microscope. In immunohistochemical assays the content of NGF in the sciatic nerve after single or repeated administration of test drugs was assessed.

RESULTS

Microscopic examinations of the sciatic nerve revealed ultrastructural changes in nerve fibers indicating for neurodegenerative processes induced by CCI. Seven days after CCI surgery LPP1 and pregabalin reduced tactile allodynia in von Frey test (ED50 values were 1.5 and 15.4 mg/kg, respectively). None of the test drugs at dose range 0.5-100 mg/kg induced motor deficits in the rotarod test. In immunohistochemical assays repeated doses of pregabalin and LPP1 elevated NGF content.

CONCLUSIONS

LPP1 has antiallodynic properties and is an interesting lead structure in the search for novel analgesics used in neuropathic pain.

A hybrid modeling approach for optimization of PMAA–chitosan–PEG nanoparticles for oral insulin delivery

This study aimed to develop pH sensitive polymethacrylic acid–chitosan–polyethylene glycol (PCP) nanoparticles for oral insulin delivery.

Black box modeling of PIDs implemented in PLCs without structural information: a support vector regression approach

In this report, the parameters identification of a proportional–integral–derivative (PID) algorithm implemented in a programmable logic controller (PLC) using support vector regression (SVR) is presented. This report focuses on a black box model of the PID with additional functions and modifications provided by the manufacturers and without information on the exact structure. The process of feature selection and its impact on the training and testing abilities are emphasized. The method was tested on a real PLC (Siemens and General Electric) with the implemented PID. The results show that the SVR maps the function of the PID algorithms and the modifications introduced by the manufacturer of the PLC with high accuracy. With this approach, the simulation results can be directly used to tune the PID algorithms in the PLC. The method is sufficiently universal in that it can be applied to any PI or PID algorithm implemented in the PLC with additional functions and modifications that were previously considered to be trade secrets. This method can also be an alternative for engineers who need to tune the PID and do not have any such information on the structure and cannot use the default settings for the known structures.

Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor

Computer-aided detection (CAD) can help radiologists to detect pulmonary nodules at an early stage. In pulmonary nodule CAD systems, feature extraction is very important for describing the characteristics of nodule candidates. In this paper, we propose a novel three-dimensional shape-based feature descriptor to detect pulmonary nodules in CT scans. After lung volume segmentation, nodule candidates are detected using multi-scale dot enhancement filtering in the segmented lung volume. Next, we extract feature descriptors from the detected nodule candidates, and these are refined using an iterative wall elimination method. Finally, a support vector machine-based classifier is trained to classify nodules and non-nodules. The performance of the proposed system is evaluated on Lung Image Database Consortium data. The proposed method significantly reduces the number of false positives in nodule candidates. This method achieves 97.5% sensitivity, with only 6.76 false positives per scan.