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Eight single (SGA) and eight multi-population (MGA) genetic algorithms (GA) differing in the sequence of implementation of the main genetic operators’ selection, crossover and mutation, or omitting the mutation operator, have been examined for the purposes of parameter identification of a Saccharomyces cerevisiae fed-batch fermentation process model. The influence of some of the main genetic algorithm parameters, namely number of individuals, maximum number of generations, generation gap, crossover and mutation rates for both SGA and MGA, and insertion and migration probability for MGA only, have been investigated in depth. Almost all applied SGA and MGA led to similar values of the optimization criterion but the SGA with operators’ sequence mutation, crossover and selection, and MGA with operators’ sequence crossover, selection and mutation, are significantly faster than others while keeping the model accuracy. Among the considered GA parameters, generation gap influences most significantly to SGA and MGA convergence time, saving of about 40% of computational time of the algorithms without affecting the model accuracy.

Alzheimer’s disease (AD) is considered a complex neurodegenerative condition which warrants the development of multitargeted drugs to tackle the key pathogenetic mechanisms of the disease. In this study, two novel series of melatonin- and donepezil-based hybrid molecules with hydrazone (3a–r) or sulfonyl hydrazone (5a–l) fragments were designed, synthesized, and evaluated as multifunctional ligands against AD-related neurodegenerative mechanisms. Two lead compounds (3c and 3d) exhibited a well-balanced multifunctional profile, demonstrating intriguing acetylcholinesterase (AChE) inhibition, promising antioxidant activity assessed by DPPH, ABTS, and FRAP methods, as well as the inhibition of lipid peroxidation in the linoleic acid system. Compound 3n, possessing two indole scaffolds, showed the highest activity against butyrylcholinesterase (BChE) and a high selectivity index (SI = 47.34), as well as a pronounced protective effect in H2O2-induced oxidative stress in SH-SY5Y cells. Moreover, compounds 3c, 3d, and 3n showed low neurotoxicity against malignant neuroblastoma cell lines of human (SH-SY5Y) and murine (Neuro-2a) origin, as well as normal murine fibroblast cells (CCL-1) that indicate the in vitro biocompatibility of the experimental compounds. Furthermore, compounds 3c, 3d, and 3n were capable of penetrating the blood–brain barrier (BBB) in the experimental PAMPA-BBB study. The molecular docking showed that compound 3c could act as a ligand to both MT1 and MT2 receptors, as well as to AchE and BchE enzymes. Taken together, those results outline compounds 3c, 3d, and 3n as promising prototypes in the search of innovative compounds for the treatment of AD-associated neurodegeneration with oxidative stress. This study demonstrates that hydrazone derivatives with melatonin and donepezil are appropriate for further development of new AChE/BChE inhibitory agents.

Tuberculosis (TB) remains a widespread infectious disease and one of the top 10 causes of death worldwide. Nevertheless, despite significant advances in the development of new drugs against tuberculosis, many therapies and preventive measures do not lead to the expected favorable health results for various reasons. The aim of this study was to evaluate the acute and sub-acute toxicity and oxidative stress of two selected nitrofuranyl amides with high in vitro antimycobacterial activity. In addition, molecular docking studies were performed on both compounds to elucidate the possibilities for further development of new anti-tuberculosis candidates with improved efficacy, selectivity, and pharmacological parameters. Acute toxicity tests showed that no changes were observed in the skin, coat, eyes, mucous membranes, secretions, and vegetative activity in mice. The histological findings include features consistent with normal histological architecture without being associated with concomitant pathological conditions. The observed oxidative stress markers indicated that the studied compounds disturbed the oxidative balance in the mouse liver. Based on the molecular docking, compound DO-190 showed preferable binding energies compared to DO-209 in three out of four targets, while both compounds showed promising protein–ligand interactions. Thus, both studied compounds displayed promising activity with low toxicity and can be considered for further evaluation and/or lead optimization.

The experimental procedures of drug design, proven to be time-consuming and costly, are successfully complemented with computer-aided (in silico) approaches nowadays. Virtual ligand screening (VLS) is one of the most promising approaches when searching for new hit compounds. The efficiency of VLS procedures might be improved via post-docking optimization. In the focus of this investigation is AMMOS (Automatic Molecular Mechanics Optimization for in silico Screening) developed as multi-step structure-based procedure for efficient computational refinement of protein-ligand complexes at different levels of protein flexibility. Their performance has been assessed by the recently developed InterCriteria Analysis (ICrA), elaborated as multi-criterion decision-making approach to reveal possible relations in the behavior of pairs of criteria when multiple objects are considered. The capacity of ICrA as a supporting tool to assess the effect of applying different levels of protein flexibility in the post-docking optimization via AMMOS has been investigated and analyzed.

This paper presents a hybrid scheme based on two population-based metaheuristic techniques, namely genetic algorithm (GA) and cuckoo search (CS). In particular, the hybrid is formed by the application of standard simple genetic algorithms (SGA) and CS, specifically adapted and for first time applied by the authors for the purposes of parameter identification of yeast fed-batch fermentation process model. The parameters of the hybrid technique SGA-CS have been thoroughly explored and tuned to meet the specific peculiarities of the considered here optimization problem. A comparison of SGA, CS and developed hybrid SGA-CS has been performed, outlining the advantages and disadvantages of each algorithm. Additionally, a new modification of SGA-CS hybrid technique, inspired by proven as very effective modification of SGA, working with implementation of main genetic operators in order crossover, mutation and selection, has been here elaborated. Presented modified hybrid technique has been tested, aiming at verification of the obtained promising results of developed SGA-CS technique.

Sdox is a hydrogen sulfide (H2S)-releasing doxorubicin effective in P-glycoprotein-overexpressing/doxorubicin-resistant tumor models and not cytotoxic, as the parental drug, in H9c2 cardiomyocytes. The aim of this study was the assessment of Sdox drug-like features and its absorption, distribution, metabolism, and excretion (ADME)/toxicity properties, by a multi- and transdisciplinary in silico, in vitro, and in vivo approach. Doxorubicin was used as the reference compound. The in silico profiling suggested that Sdox possesses higher lipophilicity and lower solubility compared to doxorubicin, and the off-targets prediction revealed relevant differences between Dox and Sdox towards several cancer targets, suggesting different toxicological profiles. In vitro data showed that Sdox is a substrate with lower affinity for P-glycoprotein, less hepatotoxic, and causes less oxidative damage than doxorubicin. Both anthracyclines inhibited CYP3A4, but not hERG currents. Unlike doxorubicin, the percentage of zebrafish live embryos at 72 hpf was not affected by Sdox treatment. In conclusion, these findings demonstrate that Sdox displays a more favorable drug-like ADME/toxicity profile than doxorubicin, different selectivity towards cancer targets, along with a greater preclinical efficacy in resistant tumors. Therefore, Sdox represents a prototype of innovative anthracyclines, worthy of further investigations in clinical settings.

The concept of an Intuitionistic Fuzzy Modal Topological Structure (IFMTS) was introduced previously, and some of its properties were studied. So far, there are two different IFMTSs based on the classical intuitionistic fuzzy operations: “union” (∪) and “intersection” (∩). In the present paper, two new IFMTSs are developed. They are based on new intuitionistic fuzzy topological operators from closure and interior types, introduced here for the first time, and on the two standard intuitionistic fuzzy modal operators □ and ◊. Some basic properties of the new IFMTSs are discussed. The newly presented IFMTSs could be considered as a basis for the next research on the IFMTSs. Some ideas for the future development of the IFMTS theory and open problems are formulated, related to the existence of other intuitionistic fuzzy operations that can generate new intuitionistic fuzzy topological operators and, respectively, new IFMTSs.

The concept of an intuitionistic fuzzy deep neural network (IFDNN) is introduced here as a demonstration of a combined use of artificial neural networks and intuitionistic fuzzy sets, aiming to benefit from the advantages of both methods. The investigation presents in a methodological way the whole process of IFDNN development, starting with the simplest form—an intuitionistic fuzzy neural network (IFNN) with one layer with single-input neuron, passing through IFNN with one layer with one multi-input neuron, further subsequent complication—an IFNN with one layer with many multi-input neurons, and finally—the true IFDNN with many layers with many multi-input neurons. The formulas for strongly optimistic, optimistic, average, pessimistic and strongly pessimistic formulas for NN parameters estimation, represented in the form of intuitionistic fuzzy pairs, are given here for the first time for each one of the presented IFNNs. To demonstrate its workability, an example of an IFDNN application to biomedical data is here presented.

(1) Background: In silico approaches to rational drug design are among the fastest evolving ones. Depending on the available structural information for the biomacromolecule and the small molecule, the in silico methods are classified as ligand- and structure-based. The latter predict ligand–receptor binding using 3D structures of both molecules, whose computational simulation is referred to as molecular docking. It aims at estimating the binding affinity (approximated by scoring function) and the ligand binding pose in the receptor’s active site, which postulates a key role of the scoring functions in molecular docking algorithms. This study focuses on the performance of different types of scoring functions implemented in molecular modelling software packages. (2) Methods: An InterCriteria analysis (ICrA) was applied to assess the performance of the scoring functions available in MOE, GOLD, SeeSAR, and AutoDock Vina software platforms. The InterCriteria analysis was developed to distinguish possible relations between pairs of criteria when multiple objects are considered. All 12 investigated scoring functions were tested by docking a set of protease inhibitors in the binding sites of two protein targets. The dataset consisted of 88 benzamidine-type compounds with experimentally measured inhibitory constants for thrombin and trypsin, which allows for the objective assessment of the scoring functions performance. The results generated by the molecular docking were subjected to ICrA in order to analyze both docking energies as approximations of the binding affinities and RMSDs (root-mean-square deviation) as measures of the experimental binding pose proximity between the compounds and the co-crystalized ligand, based on the atoms in the common scaffold. (3) Results: The results obtained for the best poses, the average of the best 5 or 30 poses retained after docking, were analyzed. A comparison with the experimentally observed inhibitory effects was also performed. The InterCriteria analysis application confirms that the performance of the scoring functions for the same dataset of ligands depends on the studied protein. The analysis reveals that none of the studied scoring functions is a good predictor of the compounds’ binding affinities for the considered protein targets. (4) Conclusion: In terms of this analysis, the investigated scoring functions do not produce equivalent results, which suggests the necessity for their combined use in consensus docking studies.

The increased use of polyphenols nowadays poses the need for identification of their new pharmacological targets. Recently, structure similarity-based virtual screening of DrugBank outlined pseudopurpurin, a hydroxyanthraquinone from Rubia cordifolia spp., as similar to gatifloxacin, a synthetic antibacterial agent. This suggested the bacterial DNA gyrase and DNA topoisomerase IV as potential pharmacological targets of pseudopurpurin. In this study, estimation of structural similarity to referent antibacterial agents and molecular docking in the DNA gyrase and DNA topoisomerase IV complexes were performed for a homologous series of four hydroxyanthraquinones. Estimation of shape- and chemical feature-based similarity with (S)-gatifloxacin, a DNA gyrase inhibitor, and (S)-levofloxacin, a DNA topoisomerase IV inhibitor, outlined pseudopurpurin and munjistin as the most similar structures. The docking simulations supported the hypothesis for a plausible antibacterial activity of hydroxyanthraquinones. The predicted docking poses were grouped into 13 binding modes based on spatial similarities in the active site. The simultaneous presence of 1-OH and 3-COOH substituents in the anthraquinone scaffold were emphasized as relevant features for the binding modes’ variability and ability of the compounds to strongly bind in the DNA-enzyme complexes. The results reveal new potential pharmacological targets of the studied polyphenols and help in their prioritization as drug candidates and dietary supplements.

Fifteen 4-methyl-1,2,3-thiadiazole-based hydrazone derivatives 3a–d and sulfonyl hydrazones 5a–k were synthesized. They were characterized by 1H-NMR, 13C NMR, and HRMS. Mycobacterium tuberculosis strain H37Rv was used to assess their antimycobacterial activity. All compounds demonstrated significant minimum inhibitory concentrations (MIC) from 0.07 to 0.32 µM, comparable to those of isoniazid. The cytotoxicity was evaluated using the standard MTT-dye reduction test against human embryonic kidney cells HEK-293T and mouse fibroblast cell line CCL-1. 4-Hydroxy-3-methoxyphenyl substituted 1,2,3-thiadiazole-based hydrazone derivative 3d demonstrated the highest antimycobacterial activity (MIC = 0.0730 µM) and minimal associated cytotoxicity against two normal cell lines (selectivity index SI = 3516, HEK-293, and SI = 2979, CCL-1). The next in order were sulfonyl hydrazones 5g and 5k with MIC 0.0763 and 0.0716 µM, respectively, which demonstrated comparable minimal cytotoxicity. All compounds were subjected to ADME/Tox computational predictions, which showed that all compounds corresponded to Lipinski’s Ro5, and none were at risk of toxicity. The suitable scores of molecular docking performed on two crystallographic structures of enoyl-ACP reductase (InhA) provide promising insight into possible interaction with the InhA receptor. The 4-methyl-1,2,3-thiadiazole-based hydrazone derivatives and sulfonyl hydrazones proved to be new classes of lead compounds having the potential of novel candidate antituberculosis drugs.

Proximal humeral fractures are common injuries and they occur primarily in older patients. They represent 5.7% of diagnosed fractures and are the third most common fracture pattern seen in elderly. Fractures of the proximal humerus usually occur after a high or low energy fall. Due to the variety of factors, which influence the classification, and the diagnosis of these fractures, early detection is the key factor for an appropriate treatment. Accordingly, in this study we present a successful example of Generalized Nets application in traumatology and propose a novel approach to timely detection and diagnosing of proximal humeral fractures.

The aim of this study was to investigate the chemical space and interactions of natural compounds with sulfotransferases (SULTs) using ligand- and structure-based in silico methods. An in-house library of natural ligands (hormones, neurotransmitters, plant-derived compounds and their metabolites) reported to interact with SULTs was created. Their chemical structures and properties were compared to those of compounds of non-natural (synthetic) origin, known to interact with SULTs. The natural ligands interacting with SULTs were further compared to other natural products for which interactions with SULTs were not known. Various descriptors of the molecular structures were calculated and analyzed. Statistical methods (ANOVA, PCA, and clustering) were used to explore the chemical space of the studied compounds. Similarity search between the compounds in the different groups was performed with the ROCS software. The interactions with SULTs were additionally analyzed by docking into different experimental and modeled conformations of SULT1A1. Natural products with potentially strong interactions with SULTs were outlined. Our results contribute to a better understanding of chemical space and interactions of natural compounds with SULT enzymes and help to outline new potential ligands of these enzymes.

Intuitionistic fuzzy logic is the main tool in the recently developed step-wise “cross-evaluation” procedure that aims at the assessment of different optimization algorithms. In this investigation, the procedure previously applied to compare the effectiveness of two or three algorithms has been significantly upgraded to evaluate the performance of a set of four algorithms. For the first time, the procedure applied here has been tested in the evaluation of the effectiveness of genetic algorithms (GAs), which are proven as very promising and successful optimization techniques for solving hard non-linear optimization tasks. As a case study exemplified with the parameter identification of a S. cerevisiae fed-batch fermentation process model, the cross-evaluation procedure has been executed to compare four different types of GAs, and more specifically, multi-population genetic algorithms (MGAs), which differ in the order of application of the three genetic operators: Selection, crossover and mutation. The results obtained from the implementation of the upgraded intuitionistic fuzzy logic-based procedure for MGA performance assessment have been analyzed, and the standard MGA has been outlined as the fastest and most reliable one among the four investigated algorithms.

The InterCriteria Analysis (ICrA) is based on the mathematical formalisms of index matrices and intuitionistic fuzzy sets. It has been elaborated to discern possible similarities in the behavior of criteria pairs when multiple objects are considered, allowing also the accounting of information uncertainty. The focus of this study is to validate the applicability of ICrA over a large set of ECG criteria extracted for arrhythmia analysis and to evaluate its ability to support the pre-selection of criteria that could be further involved in decision making procedures. ICrA is applied over 88 ECG criteria (resulting in 3828 criteria pairs) calculated for 8528 ECGs from PhysioNet/CinC Challenge 2017 database. Three criteria pairs show strong positive consonance, another 26—positive consonance, while another 15 are in negative consonance. ICrA also reveals lack of dependencies in 98 criteria pairs. The correspondence between our observations (high degrees of agreement/disagreement and lack of dependencies) and our expectations based on knowledge of the principles involved in the computation of the ECG criteria validates the application of ICrA for reliable evaluation of the relation between different criteria. This potential of ICrA to highlight useful relations between ECG criteria makes it suitable in the ECG pre-processing stage for criteria pre-selection. Thus, optimization of the feature space could be achieved together with minimization of the computations’ complexity.

The cytotoxicity and microbicidal capacity of seven organic solvents commonly applied for studying plant extracts and bioactive compounds were systematically investigated based on international standards. Four cell lines of normal (CCL-1, HaCaT) or tumor (A-375, A-431) tissue origin, seven bacterial and one fungal strain were used. The impact of the least toxic solvents in the determination of in vitro cytotoxicity was evaluated using a standardized extract from Vaccinium macrocarpon containing 54.2% v/v proanthocyanidins (CystiCran®). The solvents ethanol, methoxyethanol and polyethylene glycol were the least cytotoxic to all cell lines, with a maximum tolerated concentration (MTC) between 1 and 2% v/v. Ethanol, methanol and polyethylene glycol were mostly suitable for antimicrobial susceptibility testing, with minimum inhibitory concentrations (MICs) ≥ 25% v/v. The MTC values of the solvents dimethyl sulfoxide, dimethoxyethane and dimethylformamide varied from 0.03% to 1.09% v/v. The MICs of dimethyl sulfoxide, methoxyethanol and dimethoxyethane were in the range of 3.125–25% v/v. The cytotoxic effects of CystiCran® on eukaryotic cell lines were directly proportional to the superimposed effect of the solvents used. The results of this study can be useful for selecting the appropriate solvents for in vitro estimation of the cytotoxic and growth inhibitory effects of bioactive molecules in eukaryotic and prokaryotic cells.

Silymarin is the standardized extract from the fruits of Silybum marianum (L.) Gaertn., a well-known hepatoprotectant and antioxidant. Recently, bioactive compounds of silymarin, i.e., silybins and their 2,3-dehydro derivatives, have been shown to exert anticancer activities, yet with unclear mechanisms. This study combines in silico and in vitro methods to reveal the potential interactions of optically pure silybins and dehydrosilybins with novel protein targets. The shape and chemical similarity with approved drugs were evaluated in silico, and the potential for interaction with the Hedgehog pathway receptor Smoothened (SMO) and BRAF kinase was confirmed by molecular docking. In vitro studies on SMO and BRAF V600E kinase activity and in BRAF V600E A-375 human melanoma cell lines were further performed to examine their effects on these proteins and cancer cell lines and to corroborate computational predictions. Our in silico results direct to new potential targets of silymarin constituents as dual inhibitors of BRAF and SMO, two major targets in anticancer therapy. The experimental studies confirm that BRAF kinase and SMO may be involved in mechanisms of anticancer activities, demonstrating dose-dependent profiles, with dehydrosilybins showing stronger effects than silybins. The results of this work outline the dual SMO/BRAF effect of flavonolignans from Silybum marianum with potential clinical significance. Our approach can be applied to other natural products to reveal their potential targets and mechanism of action.

Experimental investigations in different mixing conditions (impulse and vibromixing) in a Saccharomyces cerevisiae batch cultivation are presented in this paper. The investigation is carried out in a 5 l laboratory bioreactor (working volume 3 l). Mathematical models of the process for the two mixing systems are developed. The obtained results have shown that the models are adequate and will be used for process optimisation for the two mixing systems.

A new functional state, namely dissolved oxygen limitation state for both bacteria Escherichia coli and yeast Saccharomyces cerevisiae fed-batch cultivation processes is presented in this study. Functional state modelling approach is applied to cultivation processes in order to overcome the main disadvantages of using global process model, namely complex model structure and a big number of model parameters. Alongwith the newly introduced dissolved oxygen limitation state, second acetate production state and first acetate production state are recognized during the fed-batch cultivation of E. coli, while mixed oxidative state and first ethanol production state are recognized during the fed-batch cultivation of S. cerevisiae. For all mentioned above functional states both structural and parameter identification is here performed based on experimental data of E. coli and S. cerevisiae fed-batch cultivations.

An implementation of sliding mode control for yeast fed-batch cultivation is presented in this paper. Developed controller has been implemented on two real fed-batch cultivations of Saccharomyces cerevisiae. The controller successfully stabilizes the process and shows a very good performance at high input disturbances.

An implementation of functional state approach for modelling of yeast fed-batch cultivation is presented in this paper. Using of functional state modelling approach aims to overcome the main disadvantage of using global process model, namely complex model structure and big number of model parameters, which complicate the model simulation and parameter estimation. This approach has computational advantages, such as the possibility to use the estimated values from the previous state as starting values for estimation of parameters of a new state. The functional state modelling approach is applied here for fedbatch cultivation of Saccharomyces cerevisiae. Four functional states are recognised and parameter estimation of local models is presented as well.

In order to render the specific peculiarities of the fermentation processes, as well as to avoid the complexity of mathematical description with systems of differential equations, the elaboration of some new methods and approaches for their modelling and control is predetermined. As a new, alternative approach for modelling of fermentation processes, an application of generalized nets is presented in this paper. The theory of generalized nets is applied to the fermentation process of Brevibacterium flavul 22LD for L-lysine production. A generalized net model of considered process is developed. For comparison and completeness, model with differential equations is also provided. The generalized nets model developed for the fed-batch cultivation of Brevibacterium flavul 22LD allows changing the concentration of the feeding solution and the aeration rate. In this way some inhibition effects are prevented and a possibility for optimal carrying out of the considered fermentation process is provided.

The implementation of functional state approach for modelling of yeast cultivation is considered in this paper. This concept helps in monitoring and control of complex processes such as bioprocesses. Using of functional state modelling approach for fermentation processes aims to overcome the main disadvantage of using global process model, namely complex model structure and big number of model parameters. The main advantage of functional state modelling is that the parameters of each local model can be separately estimated from other local models parameters. The results achieved from batch, as well as from fed-batch, cultivations are presented.

This paper presents the use of genetic algorithms for identification of Escherichia coli fed-batch fermentation process. Genetic algorithms are a directed random search technique, based on the mechanics of natural selection and natural genetics, which can find the global optimal solution in complex multidimensional search space. The dynamic behavior of considered process has known nonlinear structure, described with a system of deterministic nonlinear differential equations according to the mass balance. The parameters of the model are estimated using genetic algorithms. Simulation examples for demonstration of the effectiveness and robustness of the proposed identification scheme are included. As a result, the model accurately predicts the process of cultivation of E. coli.

Fermentation processes by nature are complex, time-varying, and highly nonlinear. As dynamic systems their modeling and further high-quality control are a serious challenge. The conventional optimization methods cannot overcome the fermentation processes peculiarities and do not lead to a satisfying solution. As an alternative, genetic algorithms as a stochastic global optimization method can be applied. For the purpose of parameter identification of a fed-batch cultivation of S. cerevisiae altogether four kinds of simple and four kinds of multipopulation genetic algorithms have been considered. Each of them is characterized with a different sequence of implementation of main genetic operators, namely, selection, crossover, and mutation. The influence of the most important genetic algorithm parameters—generation gap, crossover, and mutation rates has—been investigated too. Among the considered genetic algorithm parameters, generation gap influences most significantly the algorithm convergence time, saving up to 40% of time without affecting the model accuracy.