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.

Therapeutic Potential of Selenium Nanoparticles on Letrozole-Induced Polycystic Ovarian Syndrome in Female Wistar Rats.

Polycystic ovarian syndrome (PCOS) is considered the most frequent gynecological endocrine disorder that causes anovulatory infertility. The current study aimed to investigate the potential significance of selenium nanoparticles (SeNPs), an IL-1 inhibitor, in the treatment of letrozole-induced PCOS in rats that satisfied the metabolic and endocrine parameters found in PCOS patients. Letrozole (2 ppm, per orally, p.o.) was given orally to female Wistar rats for 21 days to develop PCOS. After PCOS induction, rats were given SeNPs (25 ppm/day, p.o.), SeNPs (50 ppm/day, p.o.), or metformin (2 ppm/day, p.o.) for 14 days. PCOS was associated with an increase in body weight, ovarian weight, ovarian size, and cysts, as well as an increase in blood testosterone, luteinizing hormone (LH), and insulin, glycaemia, and lipid profile levels. The SeNP administration decreased all of these variables. Furthermore, SeNPs significantly reduced letrozole-induced oxidative stress in the ovaries, muscles, and liver by decreasing elevated levels of malondialdehyde and total nitrite while raising suppressed levels of superoxide dismutase and catalase. SeNPs increased the amounts of the protective proteins Kelch-like ECH-associated protein 1 (Keap-1), nuclear factor erythroid 2-related factor 2 (Nrf2), and OH-1. It was depicted from the study that SeNPs reduce the upregulation of inflammatory cytokines that are interleukin 6 (IL-6), tumour necrosis factor α (TNF-α), and the interleukin 1 (IL-1). Our findings show that SeNPs, through their antioxidant and anti-inflammatory characteristics, alleviate letrozole-induced PCOS.

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.

Identification of differentially expressed genes and pathways crosstalk analysis in Rheumatoid and Osteoarthritis using next-generation sequencing and protein-protein networks.

Osteoarthritis occurs when protective cartilage of bones worn out. Similarlty, cartilage damage occurs mainly in the pannus cartilage in rheumatoid arthritis. It is a potentially debilitating condition, affecting women two to three times more often than men. The cause and prognosis of rheumatoid and osteoarthritis are still poorly known. However, advances in the study of disease pathogenesis have encouraged the creation of new therapeutics with improved outcomes. The purpose of this study is to investigate the differentially expressed genes potentially involved in dysregulated rheumatoid arthritis (RA) and their association to other types of arthritis, including osteoarthritis (OA). Complete RNAs were isolated for RNA expression profiling using next-generation sequencing from human primary cultured normal and RA chondrocytes. From RNA sequencing results 250 differentially expressed genes were identified using bioinformatics analysis, of which 32 were found to be significantly playing role in RA pathogenesis and its associated diseases. Molecular ontologies of the identified genes showed they are connected to Innate immune response, Protein phosphorylation, Transcription initiation from RNA polymerase II promoter, Immune response, Neoplasms of bones, as well as osteorthritis, and Rheumatoid arthritis. Among the identified genes, TRAF1, TRAF2, BAMP, STX11, MEOX2, AES, REL, FHL3, PNMA1, SGTA, LZTS2, SIAH2, PNMA1, and TFCP2 were found to be highly enriched in the protein-protein interaction network. The significant cross talks were found in Hypertrophic cardiomyopathy, Small cell lung cancer, Proteasome, p53 signaling pathway, Arrhythmogenic right ventricular cardiomyopathy, Small cell lung cancer, SNARE interactions in vesicular transport, RIG-I-like receptor signaling pathway, and Hypertrophic cardiomyopathy pathways. The results offer new opportunities for target gene control in RA and OA cartilage destruction.

In silico analysis of quranic and prophetic medicinals plants for the treatment of infectious viral diseases including corona virus.

Coronavirus disease (COVID-19) is an infection of the respiratory system caused by single standard RNA viruses named as Severe Acute Respiratory Syndrome 2 (SARS-CoV-2). The disease appeared as a serious problem and the leading cause of death in human beings throughout the world. The main source of different phytochemicals are plants, which helps in the development of new drugs against various ailments. Islam is comprehensive religion and a complete code of life for Muslims. The teaching of Islam, according to the Holy Quran and Hadith are universal for the benefit of humanity. Islam believes that every ailment is from God and who made the disease definitely made its medication. There is a complete guideline with regard to taking measures against infectious diseases such as quarantine and seeking medicinal treatment. The research objective is to gather the knowledge of medicinal plants described in the Holy Quran or utilized by the Prophet (SAW) for the treatment of different ailments or advised to use them to boost immunity and strengthen the body. Scientists across the globe have found these plants beneficial for many diseases and have antiviral potential. In present study, the six plant species including Olea europaea, Nigella sativa, Allium Sativum, Allium cepa, Zingiber officinale and Cassia senna were selected which contain phytochemicals like Calcium Elenolate, Thymoquinone, S-Allylcysteine, Dipropyl Disulfide, Sesquiterpene, Monoterpene, Pelargonidin 3-Galactoside ion and Kaempferol. The phytochemicals monoterpene (from Zingiber officinale) shows best interaction with target proteins RdRP, 3CLPro, ACE2. Calcium Elonate (from olive) bonds with 3CLPro, ACE2 and Kemoferol and Pelargomidine (from Senna Makki) bonds with RdRP, ACE2. The ligands show a unique set of intersections i.e. hydrogen bonding, and alkyl interaction. These medicinal plants can be utilized immediately for the treatment of COVID-19 as their safety is already established. This treatment can enhance recovery when combined with other treatments. Furthermore, the screening of bioactive compounds or phytochemicals found in these plants can be utilized to design new therapeutic drug to treat COVID-19 pandemic.

In-silico network-based analysis of drugs used against COVID-19: Human well-being study.

INTRODUCTION: Researchers worldwide with great endeavor searching and repurpose drugs might be potentially useful in fighting newly emerged coronavirus. These drugs show inhibition but also show side effects and complications too. On December 27, 2020, 80,926,235 cases have been reported worldwide. Specifically, in Pakistan, 471,335 has been reported with inconsiderable deaths. PROBLEM STATEMENT: Identification of COVID-19 drugs pathway through drug-gene and gene-gene interaction to find out the most important genes involved in the pathway to deal with the actual cause of side effects beyond the beneficent effects of the drugs. METHODOLOGY: The medicines used to treat COVID-19 are retrieved from the Drug Bank. The drug-gene interaction was performed using the Drug Gene Interaction Database to check the relation between the genes and the drugs. The networks of genes are developed by Gene MANIA, while Cytoscape is used to check the active functional association of the targeted gene. The developed systems cross-validated using the EnrichNet tool and identify drug genes' concerned pathways using Reactome and STRING. RESULTS: Five drugs Azithromycin, Bevacizumab, CQ, HCQ, and Lopinavir, are retrieved. The drug-gene interaction shows several genes that are targeted by the drug. Gene MANIA interaction network shows the functional association of the genes like co-expression, physical interaction, predicted, genetic interaction, co-localization, and shared protein domains. CONCLUSION: Our study suggests the pathways for each drug in which targeted genes and medicines play a crucial role, which will help experts in-vitro overcome and deal with the side effects of these drugs, as we find out the in-silico gene analysis for the COVID-19 drugs.

Proteomic Analysis of Medicinal Plant Calotropis Gigantea by In Silico Peptide Mass Fingerprinting.

Medicinal plants are the basic source of medicinal compounds traditionally used for the treatment of human diseases. Calotropis gigantea is a medicinal plant belonging to the family of Apocynaceae in the plant kingdom and subfamily Asclepiadaceae usually bearing multiple medicinal properties to cure a variety of diseases. BACKGROUND: The Peptide Mass Fingerprinting (PMF) identifies the proteins from a reference protein database by comparing the amino acid sequence that is previously stored in the database and identified. OBJECTIVE: The purpose of the study is to identify the peptides having anti-cancerous properties by in silico peptide mass fingerprinting. METHODS: The calculation of in silico peptide masses is done through the ExPASy PeptideMass and these masses are used to identify the peptides from the MASCOT online server. Anticancer probability is calculated by iACP server, docking of active peptides is done by CABS-dock the server. RESULTS: The anti-cancer peptides are identified with the MASCOT peptide mass fingerprinting server, the identified peptides are screened and only the anti-cancer are selected. De-novo peptide structure prediction is used for 3D structure prediction by PEP-FOLD 3 server. The docking results confirm strong bonding with the interacting amino acids of the receptor protein of breast cancer BRCA1 which shows the best peptide binding to the active chain, the human leukemia protein docking with peptides shows the accurate binding. CONCLUSION: These peptides are stable and functional and are the best way for the treatment of cancer and many other deadly diseases.

Isolation and Characterization of Leishmanial Adenine Aminohydrolase as a Drug Target.

BACKGROUND: Search for new drug targets is becoming imperative these days, given that marketed chemotherapeutic drugs have lost their efficacy against harmful agents because of adaptability to climatic changes and co-evolving vectors to new hosts. In the wake of such a challenge, the prominence of biochemical studies is increasing by way of exploring selective enzymes and investigating their structural and functional properties through biochemical kinetic parameter Km for the application of IC50 using designed drugs. Recently, discovered Adenine Aminohydrolase (EC 3.5.4.2) in Leishmania has been found to be absent in mammalian purine salvage pathway and thus considered as a promising drug target against infectious agents. OBJECTIVES: The objective of this study is to isolate and characterize AAH by learning its kinetic mode of action using preferred substrate Adenine and additives estimated through expected product formation Hypoxanthine. Bioassays designed to measure exact Enzyme kinetic parameter Km value through establishing hyperbolic curve of an enzyme reaction with the use of exact values of cellular quantities for IC50 application under experimental conditions devised by presteady state approach for SSA validity. METHODS: Following saturation kinetic, the plot of hyperbolic equilibrium curve developed using initial rates of product formation as a function of (Si) through forward shift under circumstance dG0 the system allows product and reactant favored reactions in relation to (Ef) ≈ [E0 = KM] until complete saturation and estimates Km and Vmax of enzyme system under applied conditions. M-M equation used to assess experimental initial rate data for estimation of Km on excel using Solver and nonlinear least square coefficient correlation "R2" using logarithmic equation for nonlinear curve assessment. RESULTS: UV/Vis spectrophotometer selectively analyzed reacting components confirming Enzyme characteristic reaction constant Km equal toi15. 0 ± 2 µ mol acquired from the Hyperbolic curve developed through the use of exact (Si) ranges at selected parameter Km and Vmax. The curve assessed by Michaelis Menten equation provides Km value=14.99 µmol and non-linear least square coefficient correlation "R2" value equal to 0.9895, along with that optimized lysis buffer formulation. In the docked complexes, the interactive amino acids identified were MSE441, ALA 364, GLN363, MSE518, VAL362, GLY517, ASP538, ALA445, TYR521, and TYR444. 2D interactions revealed hydrophobic and alkyl interactions at the noncompetitive binding site of the enzyme and therefore recommended as potential inhibitors against 3ICS protein. CONCLUSION: This study encourages biochemical analysis of the novel enzymes with the use of presteady state rationale in association with the computational tools as an effective way of designing drugs in a short time against selective enzymes to meet the current challenge efficiently.

nCOV-19 peptides mass fingerprinting identification, binding, and blocking of inhibitors flavonoids and anthraquinone of Moringa oleifera and hydroxychloroquine.

An rare pandemic of viral pneumonia occurs in December 2019 in Wuhan, China, which is now recognized internationally as Corona Virus Disease 2019 (COVID-19), the etiological agent classified as Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2). According to the World Health Organization (WHO), it has so far expanded to more than 213 countries/territories worldwide. Our study aims to find the viral peptides of SARS-COV-2 by peptide mass fingerprinting (PMF) in order to predict its novel structure and find an inhibitor for each viral peptide. For this reason, we calculated the mass of amino acid sequences translated from the SARS-CoV2 whole genome and identify the peptides that may be a target for inhibition. Molecular peptide docking with Moringa oleifera, phytochemicals (aqueous and ethanolic) leaf extracts of flavonoids (3.56 ± 0.03), (3.83 ± 0.02), anthraquinone (11.68 ± 0.04), (10.86 ± 0.06) and hydroxychloroquine present therapy of COVID-19 in Pakistan for comparative study. Results indicate that 15 peptides of SARS-CoV2 have been identified from PMF, which is then used as a selective inhibitor. The maximum energy obtained from AutoDock Vina for hydroxychloroquine is -5.1 kcal/mol, kaempferol (flavonoid) is -6.2 kcal/mol, and for anthraquinone -6 kcal/mol. Visualization of docking complex, important effects are observed regarding the binding of peptides to drug compounds. In conclusion, it is proposed that these compounds are effective antiviral agents against COVID-19 and can be used in clinical trials.Communicated by Ramaswamy H. Sarma.

In silico authentication of amygdalin as a potent anticancer compound in the bitter kernels of family Rosaceae.

Amygdalin a naturally occurring compound, predominantly in the bitter kernels of apricot, almond, apple and other members of Rosaceae family. Though, amygdalin is used as an alternative therapy to treat various types of cancer but its role in cancer pathways has rarely been explored yet. Therefore, present study was intended with the aim to investigate the alleged anti-cancerous effects of amygdalin specifically on PI3K-AKT-mTOR and Ras pathways of cancer in human body. Computational modelling and simulation techniques were used to assess the effect of amygdalin on PI3K-AKT-mTOR and Ras pathways using different level of dosage. It was observed that amygdalin had direct and substantial contribution to regulate PI3K-mTOR activities on threshold levels while the other caner pathways were effected indirectly. Consequently, amygdalin is a down-regulator of a cancer within a specified amount and contribute considerably to reduce various types of cancer in human. Furthermore, in-vitro and in-vivo analyses of amygdalin could be of helpful to authenticate its pharmacological effects.

In silico T-cell and B-cell Epitope Based Vaccine Design Against Alphavirus Strain of Chikungunya.

BACKGROUND: Chikungunya an arbovirus, is transmitted to humans by the bite of Aedes mosquito. The virus occurrences have been reported in Southeast Asian countries including Pakistan. Its symptoms include typical febrile illness and arthralgic syndrome. The virus has not decisively proved to be life-threatening. METHODS: The attempt was to design T-cell and B-cell epitope-based vaccine for Chikungunya. The proteome of chikungunya was retrieved, antigenic proteins were identified and T-cell epitopes and B-cell epitopes were predicted. Interacting HLA alleles were also identified. The final analysis was done to confirm that predicted T-cell epitopes and B-cell epitopes can be used as a vaccine. RESULTS: About 32 T-cell epitopes and a 10mer B-cell epitope were identified. Both T-cell and Bcell epitopes demonstrated strong interactions with HLA alleles. The predicted T-cell and B-cell epitopes were docked with respective HLA alleles. The docking analysis showed that the predicted respective epitopes best fit into the binding pockets of the alleles. CONCLUSION: On the basis of this computational analysis, it is suggested that these predicted epitopes can be used as a remedy against Alphavirus strain of chikungunya. Further laboratory experiments can be conducted to determine the efficacy and stability of this work.

De-Novo Ligand Design against Mutated Huntington Gene by Ligand-based Pharmacophore Modeling Approach.

BACKGROUND: Huntington's disease is characterized by three side effects, including motor disturbances, psychiatric elements, and intellectual weakness. The onset for HD has nonlinear converse associations with the number of repeat sequences of the polyglutamine mutations, so that younger patients have a tendency for longer repeats length. This HD variation is because of the development of a polyglutamine (CAG) repeats in the exon 1 of the Huntingtin protein. METHODS: In the present study, a few derivatives utilized as a part of the treatment of HD, are used to create the pharmacophore model and based on the features of the pharmacophore model; an attempt is made to design the de-novo drug for the HD protein. HD protein structure was built and docked with the novel ligand, based on shared feature pharmacophore model, through a ligand-based pharmacophore modeling approach. RESULTS: The novel ligand contains 1 HBAs, 2 HBDs, and 2 aromatic rings. It fulfills all the properties of certain drug-likeness rules, non-toxic in nature. In the docked complex, the common interactive amino acids identified are SER 1035, ALA 1062, MET 1068, LEU 1031, and THR 1036, which confirmed the validity and stability of a ligand molecule to be used as a drug in the treatment of Huntington's disease. CONCLUSION: A novel ligand can be used in clinical trials as a drug molecule against the mutations of HD gene and in laboratory procedures for efficacy analysis.

Subtractive Proteome Mining Approach towards Unique Putative Drug Targets Identification for Salmonella typhimurium.

BACKGROUND: Salmonella typhimurium is a rod-shaped bacteria with a Gram-negative genus, belonging to the Enterobacteriaceae family of microbes, which invades the intestinal lumen of Human. Salmonella typhimurium is a root source, accounting for gastroenteritis in humans as well as in other mammals. Gastroenteritisis associated with Salmonella Typhimurium interacts with the contaminated food and water and spreads to nearby people in the area. Small intestine is attacked by Salmonella, which then enter into the bloodstream momentarily, and are responsible for millions of mortalities and morbidities around the globe. Salmonella typhimurium toxins cause gastrointestiritis due to inflammation in the stomach and intestine in infants and young children. It accounts for millions of deaths with a higher incidence rate in developing countries. METHODS: In the current research, subtractive proteome mining has been done to recognize putative drug targets. The proteome was analyzed through blast in order to exclude homologous proteins. Bacterial essential proteins were predicted and the participation of the essential genes in the metabolic pathways has been analyzed. RESULTS: 36 essential genes and 15 unique pathways have been identified as potential drug targets among the total of 1934 proteins. The location of proteins is determined as an outer membrane. 3 proteins out of 36 essential proteins are recognized as putative drug targets. CONCLUSION: In the future, virtual screening for the evaluation of novel clinical compounds for the identified proteins will be effective and valuable for Salmonella Typhimurium infection in Homo sapiens.

Study on structural insight of the analysis of negative effects of opioids analgesics in naltrexone with TLR4 Mutations.

Chronic pain has been defined as the persistence that remained for more than three months. The extent of previous time duration with the normal time of natural healing phase becomes poor and results in reduced life quality and morbidity. Opioids are well recognized therapy for pain management and the clinical prescriptions based on opioids have been defined with increasing implicating behavior among patients suffering with chronic pain. The association between the pain and immunity has long been established since the involvement of interleukin-1ß (IL-1ß) in sickness that is considered with the induced hyperalgesia. In the context of pharmacodynamics Toll like receptors (TLRs) are involved in the negative effects of opioids as analgesics. The soluble factors released by immune cells as well as from the disruptive cells bind to TLRs. This binding leads the pre and post-synaptic ends on endothelial and microglial cells that exhibit the activation of complex inhibitory and excitatory process at the synapses site. In TLRs, TLR4 is mostly reported that is strongly associated in specifically in areas of T cells and macrophages. The current study is designed to investigate the structural insights of the opioids and TLR4 interactions by using computational approach in the aspect of recognizing the chemical combinatorial factors that are involved in the pain management. This study targets that how opioids interact with TLR4 and the process of chemical interaction that leads to negative effects of opioids at neuroimmune interface as well as to investigate the extent of particular naltrexone that mediates with the negative effects of opioids.

Silver nanoparticles conjugated with Neurotrophin 3 upregulate myelin gene transcription pathway.

Mathematical modeling is the art of converting problems from the biological area into handy mathematical formulations whose theoretical and numerical analysis provides understandings about the directions and solutions to the particular problem. Recently, the combination therapy treatments have been revealed exceptionally fruitful by using mathematical modeling technique. The human nervous system is composed of axons, covered by the myelin sheath. Axons carry signals and promote myelin development. The abnormalities in myelination formation due to mutations in myelin gene result in memory disorders and impaired cognitive activities. The ERBb gene family is responsible for causing abnormalities in myelin gene. Using this knowledge, the pathway of mutated myelin gene was retrieved and its model was developed. Modeling and simulation analysis was performed to determine the level of expression of several genes. The Neurotrophin 3 ligand-coated with silver nanoparticle was induced in the model to normalize the transcription of myelin gene. It was observed that the myelin gene expression level increases from 0 after two days of NT3 induction and reaches to the maximum level on the 10th day of drug induction along with an increase in ERBb expression. This research work can be used in the future as a part of drug discovery and formulation.

Clustering based drug-drug interaction networks for possible repositioning of drugs against EGFR mutations: Clustering based DDI networks for EGFR mutations.

EGFRs are a vast group of receptor tyrosine kinases playing an important role in a number of tumors, including lungs, head and neck, breast, and esophageal cancers. A couple of techniques are being used in the process of drug design. Drug repositioning or repurposing is a rising idea that consists of distinguishing modern remedial indications for officially existing dynamic pharmaceutical compounds. Here, a novel approach of analyzing drug-drug interaction networks, based on clustering methodology is used to reposition effective compounds against mutant EGFR having G719X, exon 19 deletions/insertions, L858R, and L861Q mutations. Data about 2062 drugs are obtained, and mining is performed to filter only those drugs which fulfill Lipinski rule of five. Clustering is performed, and DDIs are built on the clusters to identify effective drug compounds. Only 1052 compounds fulfill Lipinski rule. 12 clusters are formed for 1052 drugs compounds. DDIs are developed for each cluster. Only 15 drugs are suggested to be more effective assuming strong interactions in a DDI.

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.