From data banks to data bases

Simulation Models for Prediction of Bioavailability of Medicinal Drugs-the Interface Between Experiment and Computation

The oral drug bioavailability (BA) problems have remained inevitable over the years, impairing drug efficacy and indirectly leading to eventual human morbidity and mortality. However, some conventional lab-based methods improve drug absorption leading to enhanced BA, and the recent experimental techniques are up-and-coming. Nevertheless, some have inherent drawbacks in improving the efficacy of poorly insoluble and low impermeable drugs. Drug BA and strategies to overcome these challenges were briefly highlighted. This review has significantly unravelled the different computational models for studying and predicting drug bioavailability. Several computational approaches provide mechanistic insights into the oral drug delivery system simulation of descriptors like solubility, permeability, transport protein-ligand interactions, and molecular structures. The in silico techniques have long been known still are just being applied to unravel drug bioavailability issues. Many publications have reported novel applications of the computational models towards achieving improved drug BA, including predicting gastrointestinal tract (GIT) drug absorption properties and passive intestinal membrane permeability, thus maximizing time and resources. Also, the classical molecular simulation models for free solvation energies of soluble-related processes such as solubilization, dissolutions, supersaturation, and precipitation have been used in virtual screening studies. A few of the tools are GastroPlus^TM that supports biowaiver for drugs, mainly BCS class III and predicts drug compounds' absorption and pharmacokinetic process; SimCyp® simulator for mechanistic modelling and simulation of drug formulation processes; pharmacodynamics analysis on non-linear mixed-effects modelling; and mathematical models, predicting absorption potential/maximum absorption dose. This review provides in silico-experiment annexation in the drug bioavailability enhancement, possible insights that lead to critical opinion on the applications and reliability of the various in silico models as a growing tool for drug development and discovery, thus accelerating drug development processes.

Monoplex and multiplex immunoassays: approval, advancements, and alternatives

Immunoassays are a powerful diagnostic tool and are widely used for the quantification of proteins and biomolecules in medical diagnosis and research. Enzyme-linked immunosorbent assay (ELISA) is the most commonly used immunoassay format and allows the detection of biomarkers at a very low concentration. The diagnostic platforms such as enzyme immunoassay (EIA), chemiluminescence (CL) assay, polymerase chain reaction (PCR), flow cytometry (FC), and mass spectrometry (MS) have been used to identify molecular biomarkers. However, these diagnostic tools requiring expensive equipment, long testing time, and qualified personnel that is not always available in small local hospitals with limited resources. The lateral flow immunoassay (LFIA) platform was developed for rapidly obtaining laboratory results and to make urgent decisions in emergency medicine, as well as the recently introduced concept of testing at the site of care (point-of-care, POC). The simultaneous measurement of different substances from a single sample called multiplex assays have become increasingly significant for in vitro quantification of multiple analytes in a single sample, thereby minimising cost, time, and volume. In multiplex immunoassays, the ligands are immobilized either in planar format (flat surface) or on microspheres in suspension that binds to target analytes in sample. The multiplex technology has established itself in proteomic networks and pathways, validation of genomic discoveries, and in the development of clinical biomarkers. In the present review article, various types of monoplex/simplex and complex/multiplex immunoassays have been analysed that are increasingly being applied in laboratory medicine. Also, some advantages and disadvantages of these multiplex assays have also been included such as experimental animals, in vitro tests using cell lines and tissue samples, 3-dimensional modelling and bioprinting, in silico tests, organ-on-chip, and computer modelling.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.

Computer-Aided Design and Manufacturing (CAD/CAM) for Bioprinting

Three-dimensional (3D) printing of human tissues and organs has been an exciting area of research for almost three decades [Bonassar and Vacanti. J Cell Biochem. 72(Suppl 30-31):297-303 (1998)]. The primary goal of bioprinting, presently, is achieving printed constructs with the overarching aim toward fully functional tissues and organs. Technology, in hand with the development of bioinks, has been identified as the key to this success. As a result, the place of computer-aided systems (design and manufacturing-CAD/CAM) cannot be underestimated and plays a significant role in this area. Unlike many reviews in this field, this chapter focuses on the technology required for 3D bioprinting from an initial background followed by the exciting area of medical imaging and how it plays a role in bioprinting. Extraction and classification of tissue types from 3D scans is discussed in addition to modeling and simulation capabilities of scanned systems. After that, the necessary area of transferring the 3D model to the printer is explored. The chapter closes with a discussion of the current state-of-the-art and inherent challenges facing the research domain to achieve 3D tissue and organ printing.

A multiple classifier system identifies novel cannabinoid CB2 receptor ligands

Drugs have become an essential part of our lives due to their ability to improve people's health and quality of life. However, for many diseases, approved drugs are not yet available or existing drugs have undesirable side effects, making the pharmaceutical industry strive to discover new drugs and active compounds. The development of drugs is an expensive process, which typically starts with the detection of candidate molecules (screening) after a protein target has been identified. To this end, the use of high-performance screening techniques has become a critical issue in order to palliate the high costs. Therefore, the popularity of computer-based screening (often called virtual screening or in silico screening) has rapidly increased during the last decade. A wide variety of Machine Learning (ML) techniques has been used in conjunction with chemical structure and physicochemical properties for screening purposes including (i) simple classifiers, (ii) ensemble methods, and more recently (iii) Multiple Classifier Systems (MCS). Here, we apply an MCS for virtual screening (D2-MCS) using circular fingerprints. We applied our technique to a dataset of cannabinoid CB2 ligands obtained from the ChEMBL database. The HTS collection of Enamine (1,834,362 compounds), was virtually screened to identify 48,232 potential active molecules using D2-MCS. Identified molecules were ranked to select 21 promising novel compounds for in vitro evaluation. Experimental validation confirmed six highly active hits (> 50% displacement at 10 µM and subsequent Ki determination) and an additional five medium active hits (> 25% displacement at 10 µM). Hence, D2-MCS provided a hit rate of 29% for highly active compounds and an overall hit rate of 52%.

Assessing Statistical Uncertainties of Rare Events in Reactive Molecular Dynamics Simulations

Reactive molecular dynamics (MD) simulations are a versatile tool which allow for studying reaction pathways and rates simultaneously. However, most reactions will be observed only a few times in such a simulation due to computational limitations or slow kinetics, and it is unclear how this will influence the obtained rate constants. Therefore, we propose a method based on the Poisson distribution to assess the statistical uncertainty of reaction rate constants obtained from reactive MD simulations.

Reliability of neuronal information conveyed by unreliable neuristor-based leaky integrate-and-fire neurons: a model study

We conducted simulations on the neuronal behavior of neuristor-based leaky integrate-and-fire (NLIF) neurons. The phase-plane analysis on the NLIF neuron highlights its spiking dynamics – determined by two nullclines conditional on the variables on the plane. Particular emphasis was placed on the operational noise arising from the variability of the threshold switching behavior in the neuron on each switching event. As a consequence, we found that the NLIF neuron exhibits a Poisson-like noise in spiking, delimiting the reliability of the information conveyed by individual NLIF neurons. To highlight neuronal information coding at a higher level, a population of noisy NLIF neurons was analyzed in regard to probability of successful information decoding given the Poisson-like noise of each neuron. The result demonstrates highly probable success in decoding in spite of large variability – due to the variability of the threshold switching behavior – of individual neurons.

Bacteria as computers making computers

Various efforts to integrate biological knowledge into networks of interactions have produced a lively microbial systems biology. Putting molecular biology and computer sciences in perspective, we review another trend in systems biology, in which recursivity and information replace the usual concepts of differential equations, feedback and feedforward loops and the like. Noting that the processes of gene expression separate the genome from the cell machinery, we analyse the role of the separation between machine and program in computers. However, computers do not make computers. For cells to make cells requires a specific organization of the genetic program, which we investigate using available knowledge. Microbial genomes are organized into a paleome (the name emphasizes the role of the corresponding functions from the time of the origin of life), comprising a constructor and a replicator, and a cenome (emphasizing community-relevant genes), made up of genes that permit life in a particular context. The cell duplication process supposes rejuvenation of the machine and replication of the program. The paleome also possesses genes that enable information to accumulate in a ratchet-like process down the generations. The systems biology must include the dynamics of information creation in its future developments.

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.

Approaching the function of new genes by detection of their potential upstream activation sequences in Saccharomyces cerevisiae: application to chromosome III

The systematic sequencing of the yeast genome reveals the presence of many potential genes of unknown function. One way to approach their function is to define which regulatory system controls their transcription. This can also be accomplished by the detection of an upstream activation sequence (UAS). Such a detection can be done by computer, provided that the definition of a UAS includes sufficient and precise rules. We have established such rules for the UASs of the GAL4, RAP1 (RPG box), GCN4, and the HAP2/HAP3/HAP4 regulatory proteins, as well as for a motif (PAC) frequently found upstream of the genes of the RNA polymerase A and C subunits. These rules were applied to the chromosome III DNA sequence, and gave precise predictions.

Linguistic analysis of chromosome III DNA sequence of Saccharomyces cerevisiae

The analysis of the Saccharomyces cerevisiae chromosome III DNA sequence by computer ('in silico') permits the definition of its linguistic characteristics. These characteristics include the designation of non-randomly occurring oligonucleotides, their distribution along the chromosome, and the distribution of some particular homopolymers. All these elements may contribute to the understanding of the organization of information on the chromosome.