Discovery of small-molecule PD-1/PD-L1 antagonists through combined virtual screening and experimental validation.

Inhibition of the interaction between the PD-1 protein on activated lymphocytes and the PD-L1 protein on tumors represents a novel therapeutic approach for selective activation of the innate immune response against a variety of cancers. Therefore, the present study utilized a combined virtual and experimental screening approach to screen databases of both lead-like and larger molecules for identification of novel inhibitors of PD-1/PD-L1 interaction. First, high-throughput virtual screening of ∼3.7 million lead-like molecules using a rigid-receptor docking approach against both human PD-1 and PD-L1 proteins revealed possible small-molecule tractability of PD-1, but not PD-L1, binding interface. The subsequent work, therefore, involved screening of the National Cancer Institute (NCI) compound database against the PD-1 pocket. Several NCI compounds were identified with potential to bind to the PD-1 pocket and in turn inhibit the PD-1/PD-L1 interaction. The dynamic binding behavior of these molecules was further investigated using long 100 ns molecular dynamics (MD) stimulation revealing NSC631535 to be a potentially stable binder at PD-1 interface pocket. In support of these MD data, the experimental testing of NSC631535 exhibited 50% inhibition at ∼15 µM test concentration. The observed activity of this compound is promising as despite its relatively low molecular weight (415.5 g/mol) it is still capable of inhibiting the PD-1/PD-L1 interaction having a large interface area (∼1970 Å2). In summary, our integrated computational and experimental screening led to identification of a novel PD-1 antagonist that may serve as a starting point for further optimization into more potent small-molecule PD-1/PD-L1 inhibitors for cancer immunotherapy.

Metaheuristic Spatial Transformation (MST) for accurate detection of Attention Deficit Hyperactivity Disorder (ADHD) using rs-fMRI.

Accurate detection of neuro-psychological disorders such as Attention Deficit Hyperactivity Disorder (ADHD) using resting state functional Magnetic Resonance Imaging (rs-fMRI) is challenging due to high dimensionality of input features, low inter-class separability, small sample size and high intra-class variability. For automatic diagnosis of ADHD and autism, spatial transformation methods have gained significance and have achieved improved classification performance. However, they are not reliable due to lack of generalization in dataset like ADHD with high variance and small sample size. Therefore, in this paper, we present a Metaheuristic Spatial Transformation (MST) approach to convert the spatial filter design problem into a constraint optimization problem, and obtain the solution using a hybrid genetic algorithm. Highly separable features obtained from the MST along with meta-cognitive radial basis function based classifier are utilized to accurately classify ADHD. The performance was evaluated using the ADHD200 consortium dataset using a ten fold cross validation. The results indicate that the MST based classifier produces state of the art classification accuracy of 72.10% (1.71% improvement over previous transformation based methods). Moreover, using MST based classifier the training and testing specificity increased significantly over previous methods in literature. These results clearly indicate that MST enables the determination of the highly discriminant transformation in dataset with high variability, small sample size and large number of features. Further, the performance on the ADHD200 dataset shows that MST based classifier can be reliably used for the accurate diagnosis of ADHD using rs-fMRI.Clinical relevance- Metaheuristic Spatial Transformation (MST) enables reliable and accurate detection of neuropsychological disorders like ADHD from rs-fMRI data characterized by high variability, small sample size and large number of features.

Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI).

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental problem in children. Resting state functional magnetic resonance imaging (rs-fMRI) provides an important tool in understanding the aberrant functional mechanisms in ADHD patients and assist in clinical diagnosis. Recently, spatio-temporal decomposition via spatial filtering (Fukunaga-Koontz transform, ICA) have gained attention in the analysis of fMRI time-series data. Their ability to decompose the blood oxygen level dependent (BOLD) rs-fMRI time series data into discriminative spatial and temporal components have resulted in better classification accuracy and the ability to isolate the important brain circuits responsible for the observed differences in brain activity. However, they are prone to errors in the estimation of covariance matrices due to the significant presence of atypical samples in the ADHD dataset. In this paper, we present a regularization framework to obtain a robust estimation of the covariance matrices such that the effect of atypical samples is reduced. The resulting approach called as regularized spatial filtering method (R-SFM) further uses Mahalanobis whitening to lower the effect of two-way correlations while preserving the spatial arrangement of the data in the feature extraction process. R-SFM was evaluated on the benchmark ADHD200 dataset and not only obtained a 6% improvement in classification accuracy, but also a 66.66% decrease in standard deviation over the previously developed SFM approach. Also R-SFM produces higher specificity which results in lower misclassification of ADHD, thereby reducing the risk of misdiagnosis. These results clearly show that R- SFM provides an accurate and reliable tool for detection of ADHD from BOLD rs-fMRI time series data.

Macrocyclic Compounds from Ansamycin Antibiotic Class as Inhibitors of PD1-PDL1 Protein-Protein Interaction.

The ability of tumors to escape from immune destruction is attributed to the protein-protein interaction between programmed cell death protein 1 (PD1) and programmed cell death ligand 1 (PDL1) proteins expressed by immune T cells and cancer cells, respectively. Therefore, pharmacological inhibition of the PD1-PDL1 interaction presents an important therapeutic target against a variety of tumors expressing PDL1 on their cell surface. Recently, five antibodies have been approved and several are in clinical trials against the PD1-PDL1 protein-protein interaction target. In contrast, there are very few reports of small-molecule inhibitors of PD1-PDL1 interaction, and most of them have relatively modest or weak inhibition activities, emphasizing the difficulty in designing small-molecule inhibitors against this challenging target. Therefore, we focused our attention on macrocycles that are known to exhibit target activity comparable to large macromolecules despite having molecular weights closer to small, drug-like molecules. In this context, our present study led to the identification of several macrocyclic compounds from the ansamycin antibiotics class to be inhibitors of PD1-PDL1 interaction. Importantly, one of these macrocyclic antibiotics, Rifabutin, showed an IC50 value of ca. 25 µM. This is remarkable considering it has a relatively low molecular weight and still is capable of inhibiting PD1-PDL1 protein-protein interaction whose binding interface spans over ca. 1970 Å2. Thus, these macrocycles may serve as guiding points for discovery and optimization of more potent, selective small-molecule inhibitors of PD1-PDL1 interaction, one of the most promising therapeutic targets against cancer.