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In recent years, the Internet of Things (IoT) has become one of the most familiar names creating a benchmark and scaling new heights. IoT an indeed future of the communication that has transformed the objects (things) of the real world into smarter devices. With the advent of IoT technology, this decade is witnessing a transformation from traditional agriculture approaches to the most advanced ones. Limited research has been carried out in this direction. Thus, herein we present various technological aspects involved in IoT-based cultivation. The role and the key components of smart farming using IoT were examined, with a focus on network technologies, including layers, protocols, topologies, network architecture, etc . We also delve into the integration of relevant technologies such as cloud computing, big data analytics, and the integration of IoT-based cultivation. We explored various security issues in modern IoT cultivation and also emphasized the importance of safeguarding sensitive agricultural data. Additionally, a comprehensive list of applications based on sensors and mobile devices is provided, offering refined solutions for greenhouse management. The principles and regulations established by different countries for IoT-based cultivation systems are presented, demonstrating the global recognition of these technologies. Furthermore, a selection of successful use cases and real-world scenarios and applications were presented. Finally, the open research challenges and solutions in modern IoT-based cultivation were discussed., Competing Interests: The authors declare that they have no competing interests., (© 2024 ur Rehman et al.)

Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons.

Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.

An innovative predictive model was employed to predict the key performance indicators of a full-scale wastewater treatment plant (WWTP) operated with an activated sludge treatment process. The data-driven model was obtained using data gathered from Cairo, Egypt. The proposed model consists of Random Vector Functional Link (RVFL) Networks incorporated with Manta Ray Foraging Optimizer (MRFO). RVFL is used as an advanced Artificial Neural Network (ANN) that avoids the common conventional ANN problems such as overfitting. MRFO is employed to determine the best RVFL parameters to maximize the prediction accuracy of the model. The developed MRFO-RVFL is compared with conventional RVFL to figure out the role of MRFO as an optimization tool to enhance model performance. Both models were trained and tested using experimental data measured during a long period of 222 days. This study aims to provide an accurate prediction of the most widely treated effluent indicators of BOD 5 and TSS in the wastewater treatment plants. In this study, ten well-known influent wastewater parameters, BOD 5 , TSS, and VSS, influent flow rate, pH, ambient temperature, F/M ratio, SRT, WAS, and RAS, the output BOD 5 and TSS were modeled and predicted using the integrated MRFO-RVFL algorithms and compared with the standalone RVFL model. The performance of the models was evaluated using different assessment measures such as R 2 , RMSE, and others. The obtained results of R 2 and RMSE for the MRFO-RVFL model were 0.924 and 3.528 for BOD 5 and 0.917 and 6.153 for TSS, which were much better than the results of conventional RVFL with 0.840 and 6.207 for BOD 5 and 0.717 and 10.05 for TSS. Based on the obtained results, the selective model (MRFO-RVFL) exhibited a higher performance and validity to predict the TSS and optimal BOD 5 ., (Copyright © 2021. Published by Elsevier Ltd.)

COVID-19 is a new member of the Coronaviridae family that has serious effects on respiratory, gastrointestinal, and neurological systems. COVID-19 spreads quickly worldwide and affects more than 41.5 million persons (till 23 October 2020). It has a high hazard to the safety and health of people all over the world. COVID-19 has been declared as a global pandemic by the World Health Organization (WHO). Therefore, strict special policies and plans should be made to face this pandemic. Forecasting COVID-19 cases in hotspot regions is a critical issue, as it helps the policymakers to develop their future plans. In this paper, we propose a new short term forecasting model using an enhanced version of the adaptive neuro-fuzzy inference system (ANFIS). An improved marine predators algorithm (MPA), called chaotic MPA (CMPA), is applied to enhance the ANFIS and to avoid its shortcomings. More so, we compared the proposed CMPA with three artificial intelligence-based models include the original ANFIS, and two modified versions of ANFIS model using both of the original marine predators algorithm (MPA) and particle swarm optimization (PSO). The forecasting accuracy of the models was compared using different statistical assessment criteria. CMPA significantly outperformed all other investigated models., Competing Interests: The authors report no declarations of interest., (© 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.)

COVID-19 outbreak has become a global pandemic that affected more than 200 countries. Predicting the epidemiological behavior of this outbreak has a vital role to prevent its spreading. In this study, long short-term memory (LSTM) network as a robust deep learning model is proposed to forecast the number of total confirmed cases, total recovered cases, and total deaths in Saudi Arabia. The model was trained using the official reported data. The optimal values of the model's parameters that maximize the forecasting accuracy were determined. The forecasting accuracy of the model was assessed using seven statistical assessment criteria, namely, root mean square error (RMSE), coefficient of determination (R 2 ), mean absolute error (MAE), efficiency coefficient (EC), overall index (OI), coefficient of variation (COV), and coefficient of residual mass (CRM). A reasonable forecasting accuracy was obtained. The forecasting accuracy of the suggested model is compared with two other models. The first is a statistical based model called autoregressive integrated moving average (ARIMA). The second is an artificial intelligence based model called nonlinear autoregressive artificial neural networks (NARANN). Finally, the proposed LSTM model was applied to forecast the total number of confirmed cases as well as deaths in six different countries; Brazil, India, Saudi Arabia, South Africa, Spain, and USA. These countries have different epidemic trends as they apply different polices and have different age structure, weather, and culture. The social distancing and protection measures applied in different countries are assumed to be maintained during the forecasting period. The obtained results may help policymakers to control the disease and to put strategic plans to organize Hajj and the closure periods of the schools and universities., Competing Interests: The authors report no declarations of interest., (© 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.)

Coronavirus pandemic (COVID-19) has infected more than ten million persons worldwide. Therefore, researchers are trying to address various aspects that may help in diagnosis this pneumonia. Image segmentation is a necessary pr-processing step that implemented in image analysis and classification applications. Therefore, in this study, our goal is to present an efficient image segmentation method for COVID-19 Computed Tomography (CT) images. The proposed image segmentation method depends on improving the density peaks clustering (DPC) using generalized extreme value (GEV) distribution. The DPC is faster than other clustering methods, and it provides more stable results. However, it is difficult to determine the optimal number of clustering centers automatically without visualization. So, GEV is used to determine the suitable threshold value to find the optimal number of clustering centers that lead to improving the segmentation process. The proposed model is applied for a set of twelve COVID-19 CT images. Also, it was compared with traditional k-means and DPC algorithms, and it has better performance using several measures, such as PSNR, SSIM, and Entropy., Competing Interests: The authors have declared that no competing interests exist.

Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021.)

Medical imaging techniques play a critical role in diagnosing diseases and patient healthcare. They help in treatment, diagnosis, and early detection. Image segmentation is one of the most important steps in processing medical images, and it has been widely used in many applications. Multi-level thresholding (MLT) is considered as one of the simplest and most effective image segmentation techniques. Traditional approaches apply histogram methods; however, these methods face some challenges. In recent years, swarm intelligence methods have been leveraged in MLT, which is considered an NP-hard problem. One of the main drawbacks of the SI methods is when searching for optimum solutions, and some may get stuck in local optima. This because during the run of SI methods, they create random sequences among different operators. In this study, we propose a hybrid SI based approach that combines the features of two SI methods, marine predators algorithm (MPA) and moth-?ame optimization (MFO). The proposed approach is called MPAMFO, in which, the MFO is utilized as a local search method for MPA to avoid trapping at local optima. The MPAMFO is proposed as an MLT approach for image segmentation, which showed excellent performance in all experiments. To test the performance of MPAMFO, two experiments were carried out. The first one is to segment ten natural gray-scale images. The second experiment tested the MPAMFO for a real-world application, such as CT images of COVID-19. Therefore, thirteen CT images were used to test the performance of MPAMFO. Furthermore, extensive comparisons with several SI methods have been implemented to examine the quality and the performance of the MPAMFO. Overall experimental results confirm that the MPAMFO is an efficient MLT approach that approved its superiority over other existing methods., (This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.)

COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively., Competing Interests: The authors have declared that no competing interests exist.

We propose a homomorphic search protocol based on quantum homomorphic encryption, in which a client Alice with limited quantum ability can give her encrypted data to a powerful but untrusted quantum server and let the server search for her without decryption. By outsourcing the interactive key-update process to a trusted key center, Alice only needs to prepare and encrypt her original data and to decrypt the ciphered search result in linear time. Besides, we also present a compact and perfectly secure quantum homomorphic evaluation protocol for Clifford circuits, where the decryption key can be calculated by Alice with polynomial overhead with respect to the key length.

Recently, significant attention has been devoted to vaccine-derived poliovirus (VDPV) surveillance due to its severe consequences. Prediction of the outbreak incidence of VDPF requires an accurate analysis of the alarming data. The overarching aim to this study is to develop a novel hybrid machine learning approach to identify the key parameters that dominate the outbreak incidence of VDPV. The proposed method is based on the integration of random vector functional link (RVFL) networks with a robust optimization algorithm called whale optimization algorithm (WOA). WOA is applied to improve the accuracy of the RVFL network by finding the suitable parameter configurations for the algorithm. The classification performance of the WOA-RVFL method is successfully validated using a number of datasets from the UCI machine learning repository. Thereafter, the method is implemented to track the VDPV outbreak incidences recently occurred in several provinces in Lao People's Democratic Republic. The results demonstrate the accuracy and efficiency of the WOA-RVFL algorithm in detecting the VDPV outbreak incidences, as well as its superior performance to the traditional RVFL method.

Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures.

During normal pregnancy, mothers face a unique physiological challenge in the adaptation of glucose metabolism in preparation for the metabolic stress presented by fetal development. However, the responsible mechanism remains elusive. The purpose of this study is to investigate the mechanism of the metabolic stress of glucose metabolism in pregnant women using metabolomics method.A Ultra Performance Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometer-based untargeted metabolomics study was performed to investigate the dynamic urinary signature of the intermediates of glucose metabolism in a longitudinal cohort of 232 healthy pregnant women in their first, second, and third trimesters.Twelve glucose metabolic intermediates were screened out from hundreds of candidate metabolites using partial least squares discriminant analysis models. These 12 markers were mainly involved in the metabolic pathways of insulin resistance, glycolysis/gluconeogenesis, tricarboxylic acid cycle, nonabsorbable carbohydrate metabolism, and N-glycan biosynthesis. In particular, L-acetylcarnitine, a metabolite that is beneficial for the amelioration of insulin resistance, decreased in a time-dependent manner during normal pregnancy. Moreover, thiamine pyrophosphate, an intermediate product of glycolysis/gluconeogenesis, significantly increased in the second trimester, and argininosuccinic acid and oxalosuccinic acid, intermediates involved in the tricarboxylic acid cycle, significantly decreased in the third trimester, suggesting an increased glucose demand in the maternal body during fetal development.These findings provide novel insight into the normal pregnancy-induced elevation of insulin resistance and glycolysis/gluconeogenesis, as well as the observed reduction in the aerobic oxidation of glucose.

In this study, an effective means for detecting cancer region through different types of medical image segmentation are presented and explained. We proposed a new method for cancer segmentation on the basis of fuzzy entropy with a level set (FELs) thresholding. The proposed method was successfully utilized to segment cancer images and then efficiently performed the segmentation of test ultrasound image, brain MRI, and dermoscopy image compared with algorithms proposed in previous studies. Results showed an excellent performance of the proposed method in detecting cancer image segmentation in terms of accuracy, precision, specificity, and sensitivity measures., Competing Interests: Conflict of interest: The authors report no conflicts of interest in this work.

Pregnant women are a unique group undergoing profound structural modifications in uterus, breast, adipose tissue and extracellular fluids. Amino acid metabolic stress is a unique physical process that occurs during pregnancy. Metals constitute a fundamental part of the maternal body and have a universal effect on amino acid metabolism. However, the exact interaction between metals and amino acid metabolism during pregnancy is unknown. The aim of the present study was to determine the correlations of metals with amino acid metabolic intermediates in the urine of 232 healthy pregnant women in their first, second and third trimesters during normal pregnancy. Sixteen metals in the urine of 232 healthy pregnant women in their first, second and third trimesters were quantified using inductively coupled plasma mass spectrometry (ICP-MS). An ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometer (UPLC-QTOFMS)-based metabolomics approach was conducted to detect intermediate products involved in amino acid metabolism during the entire pregnancy period. A panel regression model was established to investigate the relationship between urine metals and amino acid metabolism. Seven metals-cadmium, cobalt, copper, cesium, manganese, thallium and vanadium-showed significant association with amino acid metabolic intermediates, including 2-oxoarginine, 3-indoleacetonitrile, indole, indole-5,6-quinone, N 2-succinyl-l-glutamic acid 5-semialdehyde, N -methyltryptamine and N -succinyl-l,l-2,6-diaminopimelate, in the healthy pregnant women. These findings indicated that exposure to cadmium, cobalt, copper, cesium, manganese, thallium and vanadium significantly affected the metabolic status of tryptophan, arginine, proline, tyrosine and lysine metabolism in the maternal body during normal pregnancy.

Introduction: Bisphenol A (BPA), 2,2-bis(4-hydroxyphenyl) propane, a common industrial chemical which has extremely huge production worldwide, is ubiquitous in the environment. Human have high risk of exposing to BPA and the health problems caused by BPA exposure have aroused public concern. However, the biomarkers for BPA exposure are lacking. As a rapidly developing subject, metabolomics has accumulated a large amount of valuable data in various fields. The secondary application of published metabolomics data could be a very promising field for generating novel biomarkers whilst further understanding of toxicity mechanisms., Objectives: To summarize the published literature on the use of metabolomics as a tool to study BPA exposure and provide a systematic perspectives of current research on biomarkers screening of BPA exposure., Methods: We conducted a systematic search of MEDLINE (PubMed) up to the end of June 25, 2017 with the key term combinations of 'metabolomics', 'metabonomics', 'mass spectrometry', 'nuclear magnetic spectroscopy', 'metabolic profiling' and 'amino acid profile' combined with 'BPA exposure'. Additional articles were identified through searching the reference lists from included studies., Results: This systematic review included 15 articles. Intermediates of glycolysis, Krebs cycle, β oxidation of long chain fatty acids, pentose phosphate pathway, nucleoside metabolism, branched chain amino acid metabolism, aromatic amino acids metabolism, sulfur-containing amino acids metabolism were significantly changed after BPA exposure, suggesting BPA had a highly complex toxic effects on organism which was consistent with existing studies. The biomarkers most consistently associated with BPA exposure were lactate and choline., Conclusion: Existing metabolomics studies of BPA exposure present heterogeneous findings regarding metabolite profile characteristics. We need more evidence from target metabolomics and epidemiological studies to further examine the reliability of these biomarkers which link to low, environmentally relevant, exposure of BPA in human body.

Bacterial cell division occurs in conjunction with the formation of a cytokinetic Z-ring structure comprised of FtsZ subunits. Agents that can disrupt Z-ring formation have the potential, through this unique mechanism, to be effective against several of the newly emerging multi-drug resistant strains of infectious bacteria. 1- and 12-Aryl substituted benzo[c]phenanthridines have been identified as antibacterial agents that could exert their activity by disruption of Z-ring formation. Substituted 4- and 5-amino-1-phenylnaphthalenes represent substructures within the pharmacophore of these benzo[c]phenanthridines. Several 4- and 5-substituted 1-phenylnaphthalenes were synthesized and evaluated for antibacterial activity against Staphylococcus aureus and Enterococcus faecalis. The impact of select compounds on the polymerization dynamics of S. aureus FtsZ was also assessed., (Copyright © 2012 Elsevier Masson SAS. All rights reserved.)

Berberine is a substituted dibenzo[a,g]quinolizin-7-ium derivative whose modest antibiotic activity is derived from its disruptive impact on the function of the essential bacterial cell division protein FtsZ. The present study reveals that the presence of a biphenyl substituent at either the 2- or 12-position of structurally-related dibenzo[a,g]quinolizin-7-ium derivatives significantly enhances antibacterial potency versus Staphylococcus aureus and Enterococcus faecalis. Studies with purified S. aureus FtsZ demonstrate that both 2- and 12-biphenyl dibenzo[a,g]quinolizin-7-ium derivatives act as enhancers of FtsZ self-polymerization., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Eg5 is a kinesin whose inhibition leads to cycle arrest during mitosis, making it a potential therapeutic target in cancers. Circular dichroism and isothermal titration calorimetry of our pyrrolotriazine-4-one series of inhibitors with Eg5 motor domain revealed enhanced binding in the presence of adenosine 5'-diphosphate (ADP). Using this information, we studied the interaction of this series with ADP-Eg5 complexes using a thermal shift assay. We measured up to a 7 degrees C increase in the thermal melting (T(m)) of Eg5 for an inhibitor that produced IC(50) values of 60 and 130 nM in microtubule-dependent adenosine triphosphatase (ATPase) and cell-based cytotoxicity assays, respectively. In general, the inhibitor potency of the pyrrolotriazine-4-one series in in vitro biological assays correlated with the magnitude of the thermal stability enhancement of ADP-Eg5. The thermal shift assay also confirmed direct binding of Eg5 inhibitors identified in a high-throughput screen and demonstrated that the thermal shift assay is applicable to a range of chemotypes and can be useful in evaluating both potent (nM) and relatively weakly binding (microM) leads. Overall, the thermal shift assay was found to be an excellent biophysical method for evaluating direct binding of a large number of compounds to Eg5, and it complemented the catalytic assay screens by providing an alternative determination of inhibitor potency.

High-throughput screening of a subset of the J&J compound library containing the carboxylic acid functional group uncovered a bromophenyl derivative as a moderate potent GPR40 agonist. Chemical elaboration of this bromophenyl led to the discovery of a novel series of GPR40 agonists with submicromolar potency. Among them, 22 and 24 behaved as full agonists when compared to the endogenous GPR40 ligand linolenic acid in a functional Ca+2 flux assay in HEK cells expressing GPR40 receptor. Several GPR40 agonists have also demonstrated the ability to induce glucose-mediated insulin secretion in the mouse MIN6 pancreatic beta-cell line. Our data supports the hypothesis that GPR40 may play an important role in fatty acid-mediated glucose-dependent insulin secretion. Compound 22 exhibited good pharmacokinetic profile in rat and may serve as a good candidate for in vivo study and may help to determine if GPR40 agonists would be beneficial in the treatment of type II diabetes.

Synthesis and SAR of substituted pyrrolotriazine-4-one analogues as Eg5 inhibitors are described. Many of these analogues displayed potent inhibitory activities in the Eg5 ATPase and A2780 cell proliferation assays. In addition, pyrrolotriazine-4-one analogue 26 demonstrated in vivo efficacy in an iv P388 murine leukemia model. Both NMR and X-ray crystallographic studies revealed that these analogues bind to an allosteric site on the Eg5 protein.

N-Aryl aminothiazoles 6-9 were prepared from 2-bromothiazole 5 and found to be CDK inhibitors. In cells they act as potent cytotoxic agents. Selectivity for CDK1, CDK2, and CDK4 was dependent of the nature of the N-aryl group and distinct from the CDK2 selective N-acyl analogues. The N-2-pyridyl analogues 7 and 19 showed pan CDK inhibitory activity. Elaborated analogues 19 and 23 exhibited anticancer activity in mice against P388 murine leukemia. The solid-state structure of 7 bound to CDK2 shows a similar binding mode to the N-acyl analogues.

N-Acyl-2-aminothiazoles with nonaromatic acyl side chains containing a basic amine were found to be potent, selective inhibitors of CDK2/cycE which exhibit antitumor activity in mice. In particular, compound 21 [N-[5-[[[5-(1,1-dimethylethyl)-2-oxazolyl]methyl]thio]-2-thiazolyl]-4-piperidinecarboxamide, BMS-387032], has been identified as an ATP-competitive and CDK2-selective inhibitor which has been selected to enter Phase 1 human clinical trials as an antitumor agent. In a cell-free enzyme assay, 21 showed a CDK2/cycE IC(50) = 48 nM and was 10- and 20-fold selective over CDK1/cycB and CDK4/cycD, respectively. It was also highly selective over a panel of 12 unrelated kinases. Antiproliferative activity was established in an A2780 cellular cytotoxicity assay in which 21 showed an IC(50) = 95 nM. Metabolism and pharmacokinetic studies showed that 21 exhibited a plasma half-life of 5-7 h in three species and moderately low protein binding in both mouse (69%) and human (63%) serum. Dosed orally to mouse, rat, and dog, 21 showed 100%, 31%, and 28% bioavailability, respectively. As an antitumor agent in mice, 21 administered at its maximum-tolerated dose exhibited a clearly superior efficacy profile when compared to flavopiridol in both an ip/ip P388 murine tumor model and in a s.c./i.p. A2780 human ovarian carcinoma xenograft model.

High throughput screening identified 2-acetamido-thiazolylthio acetic ester 1 as an inhibitor of cyclin-dependent kinase 2 (CDK2). Because this compound is inactive in cells and unstable in plasma, we have stabilized it to metabolic hydrolysis by replacing the ester moiety with a 5-ethyl-substituted oxazole as in compound 14. Combinatorial and parallel synthesis provided a rapid analysis of the structure-activity relationship (SAR) for these inhibitors of CDK2, and over 100 analogues with IC(50) values in the 1-10 nM range were rapidly prepared. The X-ray crystallographic data of the inhibitors bound to the active site of CDK2 protein provided insight into the binding modes of these inhibitors, and the SAR of this series of analogues was rationalized. Many of these analogues displayed potent and broad spectrum antiproliferative activity across a panel of tumor cell lines in vitro. In addition, A2780 ovarian carcinoma cells undergo rapid apoptosis following exposure to CDK2 inhibitors of this class. Mechanism of action studies have confirmed that the phosphorylation of CDK2 substrates such as RB, histone H1, and DNA polymerase alpha (p70 subunit) is reduced in the presence of compound 14. Further optimization led to compounds such as water soluble 45, which possesses a favorable pharmacokinetic profile in mice and demonstrates significant antitumor activity in vivo in several murine and human models, including an engineered murine mammary tumor that overexpresses cyclin E, the coactivator of CDK2.