Pharmacokinetics-Pharmacodynamics Modeling and Evaluation of Tumor Response to Bortezomib Proteasome Inhibitor in Waldenstrom Macroglobulinemia.

BACKGROUND: Waldenstrom's macroglobulinemia (WM), also known as lymphoplasmacytic lymphoma, is a type of non-Hodgkin's lymphoma in which the malignant cells produce many macroglobulin proteins. It originates from B cells and develops in the bone marrow, where Wm cells combine to produce distinct types of blood cells, resulting in reduced volumes of red blood cells, white blood cells, and platelets, making it harder for the body to fight diseases. Chemoimmunotherapy is being used for the clinical management of WM, but new targeted agents, the BTK inhibitor ibrutinib and the proteasome inhibitor bortezomib, have shown significant improvements in patients with relapsed/refractory WM. However, given its effectiveness, drug resistance and relapse are normal, and there is little research on the pathways responsible for drug effects on the tumor. METHODS: In this study, Pharmacokinetics-pharmacodynamic simulations were done to assess the effect of the proteasome inhibitor bortezomib on the tumor. For this purpose, the Pharmacokinetics-pharmacodynamic model was developed. The model parameters were determined and calculated using the Ordinary Differential Equation solver toolbox and the least-squares function. Pharmacokinetic profiles and pharmacodynamic analysis were performed to determine the change in tumor weight associated with the use of proteasome inhibitors. RESULTS: Bortezomib and ixazomib have been found to reduce tumor weight briefly, but once the dose is reduced, the tumor begins to grow again. Carfilzomib and oprozomib had better results, and rituximab reduced tumor weight more effectively. CONCLUSION: Once validated, it is proposed that a combination of selected drugs can be evaluated in the laboratory to treat WM.

Gene expression profiling utilizing extremely sensitive CDNA arrays and enrichment-based network study of major bone cancer genes.

BACKGROUND: The gene interaction network is a set of genes interconnected by functional interactions among the genes. The gene interaction networks are studied to determine pathways and regulatory mechanisms in model organisms. In this research, the enrichment study of bone cancer-causing genes is undertaken to identify several hub genes associated to the development of bone cancer. MATERIALS AND METHODS: Data on bone cancer is obtained from mutated gene samples; highly mutated genes are selected for the enrichment analysis. Due to certain interactions with each other the interaction network model for the hub genes is developed and simulations are produced to determine the levels of expression. For the array analyses, a total of 100 tumor specimens are collected. Cell cultures are prepared, RNA is extracted, cDNA arrays probes are generated, and the expressions analysis of Hub genes is determined. RESULTS: Out of cDNA array findings, only 7 genes: CDKN2A, AKT1, NRAS, PIK3CA, RB1, BRAF, and TP53 are differentially expressed and shown as significant in the development of bone tumors, approximately 15 pathways have been identified, including pathways for non-small cell lung cancer, prostate cancer, pancreatic cancer, chronic myeloid leukemia, and glioma, consisting of all the identified 7 genes. After clinical validations of tumor samples, the IDH1 and TP53 gene revealed significant number of mutations similar to other genes. Specimens analysis showed that RB1, P53, and NRAS are amplified in brain tumor, while BRAF, CDKN2A, and AKT1 are amplified in sarcoma. Maximum deletion mutations of the PIK3CA gene are observed in leukemia. CDKN2A gene amplifications have been observed in virtually all tumor specimens. CONCLUSION: This study points to a recognizable evidence of novel superimposed pathways mechanisms strongly linked to cancer.

Insights into the Dynamic Fluctuations of the Protein HPV16 E1 and Identification of Motifs by Using Elastic Network Modeling.

BACKGROUND: Cancers of cervix, head and neck regions have been found to be associated with Human Papilloma Virus (HPV) infection. E1 protein makes an important papillomavirus replication factor. Among the ORFs of papillomaviruses, the most conserved sequence is that of the E1 ORF. It is the viral helicase with being a member of class of ATPases associated with diverse cellular activities (AAA+) helicases. The interactions of E1 with human DNA and proteins occurs in the presence of short linear peptide motifs on E1 identical to those on human proteins. METHODS: Different Motifs were identified on HPV16 E1 by using ELMs. Elastic network models were generated by using 3D structures of E1. Their dynamic fluctuations were analyzed on the basis of B factors, correlation analysis and deformation energies. RESULTS: 3 motifs were identified on E1 which can interact with Cdk and Cyclin domains of human proteins. 11 motifs identified on E1 have their CDs of Pkinase on human proteins. LIG_MYND_2 has been identified as involved in stabilizing interaction of E1 with Hsp40 and Hsp70. These motifs and amino acids comprising these motifs play a major role in maintaining interactions with human proteins, ultimately causing infections leading to cancers. CONCLUSION: Our study identified various motifs on E1 which interact with specific counter domains found in human proteins, already reported having the interactions with E1. We also validated the involvement of these specific motifs containing regions of E1 by modeling elastic networks of E1. These motif involving interactions could be used as drug targets.

Computational Method for Classification of Avian Influenza A Virus Using DNA Sequence Information and Physicochemical Properties.

Accurate and fast characterization of the subtype sequences of Avian influenza A virus (AIAV) hemagglutinin (HA) and neuraminidase (NA) depends on expanding diagnostic services and is embedded in molecular epidemiological studies. A new approach for classifying the AIAV sequences of the HA and NA genes into subtypes using DNA sequence data and physicochemical properties is proposed. This method simply requires unaligned, full-length, or partial sequences of HA or NA DNA as input. It allows for quick and highly accurate assignments of HA sequences to subtypes H1-H16 and NA sequences to subtypes N1-N9. For feature extraction, k-gram, discrete wavelet transformation, and multivariate mutual information were used, and different classifiers were trained for prediction. Four different classifiers, Naïve Bayes, Support Vector Machine (SVM), K nearest neighbor (KNN), and Decision Tree, were compared using our feature selection method. This comparison is based on the 30% dataset separated from the original dataset for testing purposes. Among the four classifiers, Decision Tree was the best, and Precision, Recall, F1 score, and Accuracy were 0.9514, 0.9535, 0.9524, and 0.9571, respectively. Decision Tree had considerable improvements over the other three classifiers using our method. Results show that the proposed feature selection method, when trained with a Decision Tree classifier, gives the best results for accurate prediction of the AIAV subtype.

Molecular Docking Studies Reveal Rhein from rhubarb (Rheum rhabarbarum) as a Putative Inhibitor of ATP-binding Cassette Super-family G member 2.

BACKGROUND: ATP-binding cassette Super-family G member 2 protein is an active ATPbinding cassette transporter with the potential to combat cancer stem cells. OBJECTIVE: Due to the lack of potential ATP-binding cassette Super-family G member 2 inhibitors, we screened natural inhibitors, which could be a safe source to control multidrug resistance by blocking the regulation of ATP-binding cassette Super-family G member 2 protein. METHODS: Three-dimensional structure of ATP-binding cassette Super-family G member 2 protein downloaded from the protein databank and chemical structures of 166 selected compounds of the training dataset were retrieved from PubChem. Drug-likeness and docking analysis was conducted to shortlist the dataset for pharmacophore generation. LigandScout 4.1.5 used for pharmacophorebased screening of Zbc library of ZINC database and Autodock Vina were utilized for molecular docking against the predicted active pocket of the target protein to evaluate the potential association of protein and ligands. The physiochemical properties of novel compounds were calculated by admetSAR respectively. RESULTS: Through pharmacophore-based screening, ZINC4098704 (Rhein) was identified as a lead compound which demonstrates the least binding energy (-8.5) and the highest binding affinity with the target protein and showed optimal physiochemical profile. This compound is highly recommended for a laboratory test to confirm its activity as an ATP-binding cassette Super-family G member 2 inhibitors. CONCLUSION: Our computer-based study systematically selected natural lead compounds, which could be effective in inhibiting ATP-binding cassette Super-family G member 2 and may help reverse the effect of multidrug resistance to increase the effectiveness of chemotherapy in cancer treatment.

Computational Analysis of Domains Vulnerable to HPV-16 E6 Oncoprotein and Corresponding Hot Spot Residues.

BACKGROUND: Human Papilloma Virus (HPV) is the primary cause of cancers in cervix, head and neck regions. Oncoprotein E6 of HPV-16, after infecting human body, alters host protein- protein interaction networks. E6 interacts with several proteins, causing the infection to progress into cervical cancer. The molecular basis for these interactions is the presence of short linear peptide motifs on E6 identical to those on human proteins. METHODS: Motifs of LXXLL and E/DLLL/V-G after identification on E6, were analyzed for their dynamic fluctuations by use of elastic network models. Correlation analysis of amino acid residues of E6 was also performed in specific regions of motifs. RESULTS: Arginine, Leucine, Glutamine, Threonine and Glutamic acid have been identified as hot spot residues of E6 which can subsequently provide a platform for drug designing and understanding of pathogenesis of cervical cancer. These amino acids play a significant role in stabilizing interactions with host proteins, ultimately causing infections and cancers. CONCLUSION: Our study validates the role of linear binding motifs of E6 of HPV in interacting with these proteins as an important event in the propagation of HPV in human cells and its transformation into cervical cancer. The study further predicts the domains of protein kinase and armadillo as part of the regions involved in the interaction of E6AP, Paxillin and TNF R1, with viral E6.

System-based strategies for p53 recovery.

The authors have proposed a systems theory-based novel drug design approach for the p53 pathway. The pathway is taken as a dynamic system represented by ordinary differential equations-based mathematical model. Using control engineering practices, the system analysis and subsequent controller design is performed for the re-activation of wild-type p53. p53 revival is discussed for both modes of operation, i.e. the sustained and oscillatory. To define the problem in control system paradigm, modification in the existing mathematical model is performed to incorporate the effect of Nutlin. Attractor point analysis is carried out to select the suitable domain of attraction. A two-loop negative feedback control strategy is devised to drag the system trajectories to the attractor point and to regulate cellular concentration of Nutlin, respectively. An integrated framework is constituted to incorporate the pharmacokinetic effects of Nutlin in the cancerous cells. Bifurcation analysis is also performed on the p53 model to see the conditions for p53 oscillation.

Evaluation of the whole body physiologically based pharmacokinetic (WB-PBPK) modeling of drugs.

The Physiologically based pharmacokinetic (PBPK) modeling is a supporting tool in drug discovery and improvement. Simulations produced by these models help to save time and aids in examining the effects of different variables on the pharmacokinetics of drugs. For this purpose, Sheila and Peters suggested a PBPK model capable of performing simulations to study a given drug absorption. There is a need to extend this model to the whole body entailing all another process like distribution, metabolism, and elimination, besides absorption. The aim of this scientific study is to hypothesize a WB-PBPK model through integrating absorption, distribution, metabolism, and elimination processes with the existing PBPK model.Absorption, distribution, metabolism, and elimination models are designed, integrated with PBPK model and validated. For validation purposes, clinical records of few drugs are collected from the literature. The developed WB-PBPK model is affirmed by comparing the simulations produced by the model against the searched clinical data. . It is proposed that the WB-PBPK model may be used in pharmaceutical industries to create of the pharmacokinetic profiles of drug candidates for better outcomes, as it is advance PBPK model and creates comprehensive PK profiles for drug ADME in concentration-time plots.

Construction and analysis of protein-protein interaction network correlated with ankylosing spondylitis.

BACKGROUND: Ankylosing spondylitis, a systemic illness is a foundation of progressing joint swelling that for the most part influences the spine. However, it frequently causes aggravation in different joints far from the spine, and in addition organs, for example, the eyes, heart, lungs, and kidneys. It's an immune system ailment that may be activated by specific sorts of bacterial or viral diseases that initiate an invulnerable reaction that don't close off after the contamination is recuperated. The particular reason for ankylosing spondylitis is obscure, yet hereditary qualities assume a huge part in this condition. The rising apparatuses of network medicine offer a stage to investigate an unpredictable illness at framework level. In this study, we meant to recognize the key proteins and the biological regulator pathways including in AS and further investigating the molecular connectivity between these pathways by the topological examination of the Protein-protein communication (PPI) system. RESULTS: The extended network including of 93 nodes and have 199 interactions respectively scanned from STRING database and some separated small networks. 24 proteins with high BC at the threshold of 0.01 and 55 proteins with large degree at the threshold of 1 have been identified. CD4 with highest BC and Closeness centrality located in the centre of the network. The backbone network derived from high BC proteins presents a clear and visual overview which shows all important regulatory pathways for AS and the crosstalk between them. CONCLUSION: The finding of this research suggests that AS variation is orchestrated by an integrated PPI network centered on CD4 out of 93 nodes. AUTHOR SUMMARY: Ankylosing spondylitis, a systemic disease is an establishment of advancing joint swelling that generally impacts the spine. Be that as it may, it as often as possible causes disturbance in various joints a long way from the spine, and what's more organs. It's a resistant framework affliction that might be actuated by particular sorts of bacterial or viral ailments that start an immune response that don't shut off after the pollution is recovered. The specific explanation behind AS is dark, yet innate qualities expect a colossal part in this condition. The rising devices of system solution offer a phase to examine an erratic ailment at structure level. In this study, we intended to perceive the key proteins and the natural controller pathways incorporating into AS. The finding of this research proposes that AS variety is organized by a coordinated PPI system focused on CD4.

ChromBiSim: Interactive chromatin biclustering using a simple approach.

Combinatorial patterns of histone modifications sketch the epigenomic locale. Specific positions of these modifications in the genome are marked by the presence of such signals. Various methods highlight such patterns on global scale hence missing the local patterns which are the actual hidden combinatorics. We present ChromBiSim, an interactive tool for mining subsets of modifications from epigenomic profiles. ChromBiSim efficiently extracts biclusters with their genomic locations. It is the very first user interface based and multiple cell type handling tool for decoding the interplay of subsets of histone modifications combinations along their genomic locations. It displays the results in the forms of charts and heat maps in accordance with saving them in files which could be used for post analysis. ChromBiSim tested on multiple cell types produced in total 803 combinatorial patterns. It could be used to highlight variations among diseased versus normal cell types of any species. AVAILABILITY: ChromBiSim is available at (http://sourceforge.net/projects/chrombisim) in C-sharp and python languages.

Mathematical Modeling of E6-p53 interactions in Cervical Cancer

Background: Cervical cancer is the third most common cancer in women throughout the world. The human papillomavirus (HPV) E6 viral protein plays an essential role in proteasomal degradation of the cancer suppressant protein p53. As a result, p53 negative regulation and apoptosis relevant activities are abrogated, facilitating development of cervical cancer. Methods: A mathematical model of E6-p53 interactions was developed using mathematical laws. In-silico simulations were carried out on CellDesigner and as a test case the small molecule drug RITA was considered for its ability to rescue the functions of tumor suppressor p53 by inhibiting E6 mediated proteasomal degradation. Results: Using a computational model we scrutinized how p53 responds to RITA, and chemical reactions of this small molecule drug were incorporated to perceive the full effects. The evolved strategy allowed the p53 response and rescue of its tumor suppressor function to be delineated, RITA being found to block p53 interactions with E6 associated proteins. Conclusion: We could develop a model of E6-p53 interactions with incorporation of actions of the small molecule drug RITA. Suppression of E6 associated proteins by RITA induces accumulation of tumor suppressant p53. Using CellDesigner to encode the model ensured that it can be easily modified and extended as more data become available. This strategy should play an effective role in the development of therapies against cancer.

On p53 revival using system oriented drug dosage design.

We propose a new paradigm in the drug design for the revival of the p53 pathway in cancer cells. It is shown that the current strategy of using small molecule based Mdm2 inhibitors is not enough to adequately revive p53 in cancerous cells, especially when it comes to the extracting pulsating behavior of p53. This fact has come to notice when a novel method for the drug dosage design is introduced using system oriented concepts. As a test case, small molecule drug Mdm2 repressor Nutlin 3a is considered. The proposed method determines the dose of Nutlin to revive p53 pathway functionality. For this purpose, PBK dynamics of Nutlin have also been integrated with p53 pathway model. The p53 pathway is the focus of researchers for the last thirty years for its pivotal role as a frontline cancer suppressant protein due to its effect on cell cycle checkpoints and cell apoptosis in response to a DNA strand break. That is the reason for finding p53 being absent in more than 50% of tumor cancers. Various drugs have been proposed to revive p53 in cancer cells. Small molecule based drugs are at the foremost and are the subject of advanced clinical trials. The dosage design of these drugs is an important issue. We use control systems concepts to develop the drug dosage so that the cancer cells can be treated in appropriate time. We investigate by using a computational model how p53 protein responds to drug Nutlin 3a, an agent that interferes with the MDM2-mediated p53 regulation. The proposed integrated model describes in some detail the regulation network of p53 including the negative feedback loop mediated by MDM2 and the positive feedback loop mediated by Mdm2 mRNA as well as the reversible represses of MDM2 caused by Nutlin. The reported PBK dynamics of Nutlin 3a are also incorporated to see the full effect. It has been reported that p53 response to stresses in two ways. Either it has a sustained (constant) p53 response, or there are oscillations in p53 concentration. The claimed dosage strategy achieves the p53 response in the first case. However, for the induction of oscillations, it is shown through bifurcation analysis that to achieve oscillating behavior of p53 inhibition of Mdm2 is not enough, rather antirepression of the p53-Mdm2 complex is also needed which leads to the need of a new drug design paradigm.

ChromClust: A semi-supervised chromatin clustering toolkit for mining histone modifications interplay.

Mining patterns of histone modifications interplay from epigenomic profiles are one of the leading research areas these days. Various methods based on clustering approaches and hidden Markov models have been presented so far with some limitations. Here we present ChromClust, a semi-supervised clustering tool for mining commonly occurring histone modifications at various locations of the genome. Applying our method to 11 chromatin marks in nine human cell types recovered 11 clusters based on distinct chromatin signatures mapping to various elements of the genome. Our approach is efficient in respect to time and space usage along with the added facility of maintaining database at the backend. It outperforms the existing methods with respect to mining patterns in a semi-supervised fashion mapping to various functional elements of the genome. It will aid in future by saving the resources of time and space along with efficiently retrieving the hidden interplay of histone combinations.

MCaVoH: A toolkit for mining, classification and visualization of human microbiota.

Human body is the home for a large number of microbes. The complexity of enterotype depends on the body site. Microbial communities in various samples from different regions are being classified on the basis of 16S rRNA gene sequences. With the improvement in sequencing technologies various computational methods have been used for the analysis of microbiome data. Despite several available machine learning techniques there is no single platform available which could provide several techniques for clustering, multiclass classification, comparative analysis and the most significantly the identification of the subgroups present within larger groups of human microbial communities. We present a tool named MCaVoH for this purpose which performs clustering and classification of 16S rRNA sequence data and highlight various groups. Our tool has an added facility of biclustering which produces local group of communities present within larger groups (clusters). The core objective of our work was to identify the interaction between various bacterial species along with monitoring the composition and variations in microbial communities. MCaVoH also evaluates the performance and efficiency of different techniques using comparative analysis. The results are visualized through different plots and graphs. We implemented our tool in MATLAB. We tested our tool on several real and simulated 16S rRNA data sets and it outperforms several existing methods. Our tool provides a single platform for using multiple clustering, classification algorithms, local community identification along with their comparison which has not been done so far. Tool is available at https://sourceforge.net/projects/mcavoh/.