FGMA: A Functional Group-enhanced Deep Learning Method for Drug Toxicity Prediction.

Lhasa Limited have had a role in the in silico prediction of drug and other chemical toxicity for over 30years. This role has always been multifaceted, both as a provider of predictive software such as Derek Nexus, and as an honest broker for the sharing of proprietary chemical and toxicity data. A changing regulatory environment and the drive for the Replacement, Reduction and Refinement (the 3Rs) of animal testing have led both to increased acceptance of in silico predictions and a desire for the sharing of data to reduce duplicate testing. The combination of these factors has led to Lhasa Limited providing a suite of products and coordinating numerous data-sharing consortia that do indeed facilitate a significant reduction in the testing burden that companies would otherwise be laboring under. Many of these products and consortia can be organized into workflows for specific regulatory use cases, and it is these that will be used to frame the narrative in this chapter.

Correlation between Targeted Protein and Drug Side Effects: A Step towards the Prediction of Drug Toxicity

Linear drug toxicity models like therapeutic index (TI), physicochemical rules (rule of five, 3/75), ligand efficiency indices (LEI), ideal pharmacokinetic (PK) and pharmacodynamic (PD) profiles are widely used in drug discovery and development. In spite of this, predicting drug toxicity at various stages remains challenging and the overall productivity (<20%) and ultimate benefit to the patients remain low. A simple drug toxicity model, "Drug Toxicity Index" (DTI), is developed here using 711 oral drugs. DTI redefines drug toxicity as scaled biphasic and exponential functions of PD, PK and physicochemical parameters. PD, PK and physicochemical toxicity contributions were estimated from the on and off target IC50, maximum unbound plasma drug concentration (free C max), and log D values, respectively. These contributions are then scaled by molar dose and oral bioavailability and the logarithm of the sum of scaled contributions is DTI. Drugs with DTI above the WHO ATC drug category specific average values consistently have toxic profiles, while drugs with DTI below this average are relatively safe. DTI performs better than standard rules for lead optimization, LEI and exposure based TIs in identifying safe and toxic drugs. DTI classifies 392 drugs reported in the US-FDA's Liver Toxicity Knowledge Base (LTKB) with an AUC for ROC curves of 0.91-0.64 for different WHO ATC categories. DTI has been used to predict network meta-analysis results on relative toxicity within/across eight different therapeutic areas. It is useful in understanding PD, PK and physicochemical toxicity contributions and identifying potentially toxic drugs and the toxicity of recently approved drugs. Decision trees are proposed for applying the DTI concept in preclinical drug discovery and clinical trial settings. DTI can potentially reduce failure in drug discovery and might be useful in therapeutic drug monitoring and in xenobiotic and environmental toxicity studies.

Reference high-efficiency nonpolar packed columns for the gas—liquid chromatography of nanogram amounts of steroids part I. Retention time data

3 Some Pyrimidines of Biological and Medicinal Interest—I

• QSARs are able to predict acute oral GHS classifications conservatively. • CATMoS, VEGA and TEST predictions were combined in the form of consensus model. • The conservative consensus model (CCM) prediction was health protective. • No chemical classes or functional groups were significantly underpredicted. Consensus approaches are applied in different quantitative structure–activity relationship (QSAR) modeling contexts based on the assumption that combining individual model predictions will improve prediction reliability. This study evaluated the performance of TEST, CATMoS and VEGA models for prediction of oral rat LD 50 , both individually and in consensus, across a dataset of 6,229 organic compounds. Predicted LD 50 values from the models were compared for each compound, and the lowest value was assigned as the output of the conservative consensus model (CCM). Predictive accuracy was then evaluated based on the agreement of predicted LD 50 -based GHS category assignments with those derived experimentally. The aim was to allow for the most conservative value to be identified. Results showed that CCM had the highest over-prediction rate at 37 %, compared to TEST (24 %), CATMoS (25 %) and VEGA (8 %). Meanwhile, its under-prediction rate was lowest at 2 %, relative to TEST (20 %), CATMoS (10 %) and VEGA (5 %). Due to the method applied, CCM was the most conservative across all GHS categories. Further, structural analysis demonstrated that no specific chemical classes or functional groups were consistently underpredicted or overpredicted. The utility of CCM lies in its ability to establish a foundation for contextualizing the general use of consensus modeling, in order to derive health-protective oral rat LD 50 estimates under conditions of uncertainty, especially where experimental data are limited or absent.

cases . Cases represented at higher levels of abstraction, meta-units (non-existent meta-equipments) The organisation of cases into the library is performed according to the type of functions of the unit/meta-unit. In this way, several groups can be distinguished according to the general function type: source, transport, barrier, storage, balance, and sink . Within each functional group, units and meta-units are grouped based on their specific functions. Again, within specific functions groups, the units/meta-units are grouped based on the working function achieved. There are not distinctions between units and meta-units with the same specific function. The case library is organised by an abstract hierarchy based on function groups. This structure denotes the organisation of the functional ontology used in the framework. To retrieve cases from the case library, a similarity engine is used. Only units/meta-units of the same specific functional group are considered. The similarity engine uses functional and teleological targets to search into the library of cases. Functional and teleological models denote strongly the relationship between the units/meta-units and its neighbours. Two types of similarity are computed, local and global, which are defined as follows: 

Grafting specific functional groups onto a host catalyst to modulate its electron density is an effective strategy for enhancing heterogeneous catalytic properties. However, introducing functional groups while preserving the host's structural integrity remains a significant challenge. Herein, a multistep strategy was employed to construct a binary MOF-on-MOF catalyst (denoted as ZIF-67-on-MIL-101s), in which specific functional groups were incorporated into the multiporous structure alongside the host MOF. The experimental and theoretical calculation results show that the specific functional group of the host MOF significantly enhances the catalytic activity, leading to ZIF-67-on-MIL-101-NH2 possessing high activity on the hydrogen transfer selectively reduce nitro-compounds under mild conditions. This approach has recently emerged as a research hotspot in the fields of chemistry and materials science.

Drug toxicity and efficacy are difficult to predict partly because they are both poorly defined, which I aim to remedy here from a transcriptomic perspective. There are two major categories of drugs: (1) restorative drugs aiming to restore an abnormal cell, tissue, or organ to normal function (e.g., restoring normal membrane function of epithelial cells in cystic fibrosis), and (2) disruptive drugs aiming to kill pathogens or malignant cells. These two types of drugs require different definition of efficacy and toxicity. I outlined rationales for defining transcriptomic efficacy and toxicity and illustrated numerically their application with two sets of transcriptomic data, one for restorative drugs (treating cystic fibrosis with lumacaftor/ivacaftor aiming to restore the cellular function of epithelial cells) and the other for disruptive drugs (treating acute myeloid leukemia with prexasertib). The conceptual framework presented will help and sensitize researchers to collect data required for determining drug toxicity.

Results of our workgroups have shown that carbon fibers (CFs) are high quality biofilm carriers with well biocompatibility relative to other polymer carriers used in biofilm sewage treatment. In the present study, two kinds of microorganisms, namely, aerobic nitrifier and facultative aerobic denitrifying bacterium, were immobilized on the surface of different CFs, which the effects of specific area, functional groups and wettability on immobilization of these bacteria was studied. Results show that different microorganism require for different surface properties of CFs. Biomass of immobilized aerobic nitrifier increases with increasing specific areas, surface oxygenic functional groups and wettability of CFs. It can be immobilized more facultative aerobic denitrifying bacterium if there are proper specific areas, surface functional oxygenic group and wettability on the surface of CFs

Comprehension of drug toxicity: Software and databases

Polysaccharide molecules are constructed by long chains of monosaccharide units bound together via glycosidic linkages. Naturally occurring polysaccharides can be simply classified into four categories according to the source differences: plant polysaccharides, seaweed polysaccharides, animal polysaccharides and microbial polysaccharides (Table 1.1). Each category has its own specific structural features, e.g. most hemicelluloses as plant polysaccharides contain 6 monosaccharides (rhamnose, arabinose, galactose, glucose, xylose and mannose) while some derivatized monosaccharides, e.g. anhydrogalactose, can be found in some seaweeds polysaccharides. The molecular structure offers the most fundamental knowledge for understanding the functional, conformational and physiological properties of polysaccharides, which in turn facilitate their food and non-food applications. However, structural characterization of polysaccharides is a fairly challenging task due to the molecular complexity in terms of monosaccharide composition, glycosidic bonds (linkage patterns), degree of branching/branching position, α- or β-configurations, functional groups, molecular weight and molecular weight distribution.

Carica seeds (Carica pubescens) contain alkaloid, saponin, and flavonoid compounds, which have pharmacological activities such as antibacterial, anti-inflammatory, immunomodulatory, and antihyperlipidemic properties, but their bioavailability is low. Nanotechnology has significantly advanced in drug delivery systems because it can enhance absorption in the gastrointestinal tract, allowing entry into the bloodstream. This study aims to create and evaluate the characteristics of carica seed extract nanoparticles with varying concentrations of chitosan. The carica seed extract was obtained using the maceration method with 70% ethanol as the solvent. The Carica seed extract was then formulated into nanoparticles using the ionic gelation method with varying concentrations of chitosan: NaTPP, specifically formulas FA (0.3:1), FB (0.2:1), and FC (0.1:1). The resulting nanoparticle colloids were characterized for particle size, polydispersity index (PDI), percent transmittance (%T), and specific functional groups. The results showed that the particle sizes for formulas FA, FB, and FC were 243.7 nm, 47.96 nm, and 116.6 nm, respectively. The PDI values for formulas FA, FB, and FC were 0.378, 0.357, and 0.52, respectively. The percent transmittance for all three formulas ranged from 99.5% to 99.6%. The characterization of functional groups indicated interactions between the carica seed extract, chitosan, and NaTPP, with the presence of OH, N-H, aliphatic CH, P=O, and PO3 groups. These interactions were observed based on the shift in wavenumbers in the FTIR results for each sample. All carica seed nanoparticle formulas showed particle sizes within the nanometer range (&lt;1000 nm), polydispersity indices less than 1, percent transmittance close to 100%, and specific functional groups indicating interactions between the extract and the coating polymer. Formula FB was the optimal formula, with the smallest nanoparticle size (&lt;100 nm), a PDI value of &lt;0.5, and a percent transmittance of &gt;99%.

From studies of the rate of buildup and decay of positive and negative charges on the surfaces of vinyl and condensation polymers, a number of general observations were obtained correlating the polymer chemical structure with antistatic behavior. Polymers with functional groups such as COONa, SO3Na, HCONCH3, CONEt2, P (NMe2)3, and imidazole were found to have good antistatic properties. These functional groups were more effective in improving the antistatic properties of polymers when they were used as side chain substituents than when they were employed as part of the main polymer chain. A number of highly water‐sensitive and water‐soluble polymers were found to have extremely poor antistatic properties, indicating that there is no direct correlation of antistatic properties with hydrophilic character. A new parameter, the charge selective power of a substance, was devised to describe the charge polarity preference of a given substance. It is defined by the equation: σ = (τ+ – τ–)/τRMS, where τ+ is the halflife of charge decay for positive charge, τ− is the halflife of charge decay for negative charge, and τRMS is the root‐mean‐square halflife of charge decay. The charge selective power of a substance was found to correlate well with the assignments of substances in the tables of triboelectric series. The effect of impurities on charge decay properties and the charge selective power of polymers was found to be extremely important in influencing antistatic behavior. The synthesis of the following polymers with specific functional groups is described: polyvinylpentamethyl phosphoramide; the poly‐N‐dimethylurea derivative of polymethylolacrylamide; poly(N‐acrylyl morpholine); poly( N‐acrylyl pyrrolidine).

As infrared spectroscopy (IR) is frequently used in undergraduate organic chemistry courses, an inductive introduction to IR spectroscopy that uses index cards printed with spectra, structures, and chemical names is described. Groups of students are given an alphabetized deck of these “IR cards” to sort into functional groups. The students then determine each functional group’s infrared “signature” by comparing several spectra containing a specific functional group. Response to the IR cards was favorable, and students were more proficient in subsequent laboratories that used IR spectroscopy. This experiment is suitable for use in a one-semester or full-year organic course.