An Evaluation of the Occupational Health Hazards of Peptide Couplers

Protocol for protein modification using oxalyl thioester-mediated chemoselective ligation

The development of fast ligation chemistries for the site-specific modification of proteins has become a major focus in chemical biology. We describe steps for preparing an oxalyl thioester precursor in the form of an N-oxalyl perhydro-1,2,5-dithiazepine handle, i.e., the oxoSEA group, and incorporating it into a peptide modifier using solid phase peptide synthesis. We then detail procedures for its application for the modification of an N-terminal Cys-containing B1 domain of the streptococcal G protein using the native chemical ligation. For complete details on the use and execution of this protocol, please refer to Snella et al.1.

Capturing Differential Quality of Experimental Evidence in a Predictive Quantum-Mechanical Model for Respiratory Sensitization

Asthma is of concern in occupational toxicology with significant public-health and economic costs. In the absence of benchmark in vivo and in vitro tests, the use of mechanistically sound in silico models is critical to inform hazard and to protect workers from exposure to potentially harmful substances. We recently reported on the computer-aided discovery and REdesign (CADRE) model for respiratory sensitization, which relies on a tiered structure of expert rules, molecular simulations, quantum-mechanics calculations and advanced statistics to accurately identify respiratory sensitizers from first principles. Here, we present an update to this model based on two years of testing in the pharmaceutical space, where we captured the heterogeneity of the underlying experimental evidence in two predictive tiers, thus allowing the practitioner to select an outcome based on their expert assessment of the data reliability and relevance. This user-based tuning of predictive models is critical for end points that lack consensus on what constitutes satisfactory evidence to support a decision in the handling of chemicals for occupational safety.

General chemoselective hindered amide coupling enabled by TCFH-catalytic Oxyma and transient imine protection

We report a general chemoselective strategy for amide bond formation with poorly nucleophilic amines in the presence of reactive primary alcohols or amines as the competing nucleophiles. The selectivity for less reactive amines over competing alcohols was achieved using TCFH and catalytic Oxyma as a highly reactive, inexpensive, and safe reagent combination. By temporarily masking more reactive amines as imines through the use of electron-deficient aldehydes, the hindered amines could be similarly coupled with high efficiency and selectivity.

Outbreak of contact allergy to dicyclohexylcarbodiimide in a biotechnology laboratory

BACKGROUND

Dicyclohexylcarbodiimide (DCC) is known to cause occupational allergic contact dermatitis in biotechnology laboratory workers.

OBJECTIVES

This study aimed to assess the prevalence of DCC sensitization among workers of one biosynthesis laboratory and to determine the optimal DCC concentration for patch testing.

MATERIALS AND METHODS

23 laboratory workers frequently exposed to DCC of whom 21 suffered from dermatitis and 11 controls were enrolled into the study. All participants underwent skin patch testing with different concentrations of DCC and other substances used in the work environment. Statistical analysis was conducted to compare results between active and control groups.

RESULTS

Of the 23 workers tested, 56.5% showed positive reactions to DCC. All of them had dermatitis. Morpholine also elicited positive reactions in 26.1% of workers who were all sensitized to DCC. Dimethylformamide and chloroform yielded negative results.

CONCLUSION

Dicyclohexylcarbodiimide was a major cause of contact dermatitis in the active group indicating an outbreak of contact allergy to DCC in the company necessitating preventive measures. Both DCC 0.1% ac. and DCC 0.05% pet. should both be used for patch testing.

TCFH-NMI Ketone Synthesis Inspired by Nucleophilicity Scales

N,N,N',N'-Tetramethylchloroformamidinium hexafluorophosphate (TCFH) and N-methylimidazole (NMI) enable the facile and practical reaction of carboxylic acids with amines, alcohols, and thiols to form amides, esters, and thioesters. To develop a mild synthesis of ketones with TCFH-NMI directly from carboxylic acids at room temperature, the Mayr nucleophilicity scale was used to compare the N values of competent nucleophiles to potential carbon-centered nucleophiles, identifying pyrroles and indoles as successful substrates when N ≥ 10.

Efficient Solution-Phase Dipeptide Synthesis Using Titanium Tetrachloride and Microwave Heating

Microwaves have been successfully employed in the Lewis acid titanium tetrachloride-assisted synthesis of peptide systems. Dipeptide systems with their amino function differently protected with urethane protecting groups have been synthesized in short periods of time and with high yields. The formation of the peptide bond between the two reacting amino acids was achieved in pyridine by using titanium tetrachloride as a condensing agent and heating the reaction mixture with a microwave reactor. The reaction conditions are compatible with amino acids featuring various side chains and different protecting groups on both the amino function and side chains. Additionally, the substrates retain their chiral integrity after reaction.

Quantum-Mechanics Calculations Elucidate Skin-Sensitizing Pharmaceutical Compounds

Skin sensitization is a critical end point in occupational toxicology that necessitates the use of fast, accurate, and affordable models to aid in establishing handling guidance for worker protection. While many in silico models have been developed, the scarcity of reliable data for active pharmaceutical ingredients (APIs) and their intermediates (together regarded as pharmaceutical compounds) brings into question the reliability of these tools, which are largely constructed using publicly available nonspecialty chemicals. Here, we present the quantum-mechanical (QM) Computer-Aided Discovery and REdesign (CADRE) model, which was developed with the bioactive and structurally complex chemical space in mind by relying on the fundamentals of chemical interactions in key events (versus structural attributes of training-set data). Validated in this study on 345 APIs and intermediates, CADRE achieved 95% accuracy, sensitivity, and specificity and a combined 79% accuracy in assigning potency categories compared to the mouse local lymph node assay data. We show how historical outcomes from CADRE testing in the pharmaceutical space, generated over the past 10 years on ca. 2500 chemicals, can be used to probe the relationships between sensitization mechanisms (or the underlying chemical classes) and the probability of eliciting a sensitization response in mice of a given potency. We believe this information to be of value to both practitioners, who can use it to quickly screen and triage their data sets, as well as to model developers to fine-tune their structure-based tools. Lastly, we leverage our experimentally validated subset of APIs and intermediates to show the importance of dermal permeability on the sensitization potential and potency. We demonstrate that common physicochemical properties used to assess permeation, such as the octanol-water partition coefficient and molecular weight, are poor proxies for the more accurate energy-pair distributions that can be computed from mixed QM and classical simulations using model representations of the stratum corneum.

Fatty aldehyde bisulfite adducts as a purification handle in ionizable lipid synthesis

Rapid access to ALC-0315, a crucial component of the formulated Pfizer Covid vaccine, was obtained by employing solid adduct formation and filtration after an oxidation step in place of the standard chromatographic separation, allowing for a more scalable synthesis. Impurities were removed by formation of this fatty aldehyde bisulfite adduct at the penultimate step and by performing the final reductive amination directly with the fatty aldehyde bisulfite adduct. This eliminates chromatographic separations for all prepared aldehyde containing intermediates. Along with ALC-0315, FTT5 and SM-102 ionizable lipids were prepared utilizing this strategy. This work paves the way for more sustainable access to these critical ionizable lipids that would de-risk the world supply of important vaccines and medicines in the future.

Process Development of a Macrocyclic Peptide Inhibitor of PD-L1

This article outlines the process development leading to the manufacture of 800 g of BMS-986189, a macrocyclic peptide active pharmaceutical ingredient. Multiple N-methylated unnatural amino acids posed challenges to manufacturing due to the lability of the peptide to cleavage during global side chain deprotection and precipitation steps. These issues were exacerbated upon scale-up, resulting in severe yield loss and necessitating careful impurity identification, understanding the root cause of impurity formation, and process optimization to deliver a scalable synthesis. A systematic study of macrocyclization with its dependence on concentration and pH is presented. In addition, a side chain protected peptide synthesis is discussed where the macrocyclic protected peptide is extremely labile to hydrolysis. A computational study explains the root cause of the increased lability of macrocyclic peptide over linear peptide to hydrolysis. A process solution involving the use of labile protecting groups is discussed. Overall, the article highlights the advancements achieved to enable scalable synthesis of an unusually labile macrocyclic peptide by solid-phase peptide synthesis. The sustainability metric indicates the final preparative chromatography drives a significant fraction of a high process mass intensity (PMI).

Beyond Amide Bond Formation: TCFH as a Reagent for Esterification

In this Communication, an investigation of the combination of N,N,N',N'-tetramethylchloroformamidinium hexafluorophosphate (TCFH) and N-methylimidazole (NMI) for the synthesis of esters and thioesters is described. This work revealed the unique challenges of the reactions of less nucleophilic alcohols and more reactive thiols with the N-acyl imidazolium intermediate and led to the identification of general enabling conditions that provide high yields and selectivity for a range of alcohols and thiols.

Process Mass Intensity (PMI): A Holistic Analysis of Current Peptide Manufacturing Processes Informs Sustainability in Peptide Synthesis

Small molecule therapeutics represent the majority of the FDA-approved drugs. Yet, many attractive targets are poorly tractable by small molecules, generating a need for new therapeutic modalities. Due to their biocompatibility profile and structural versatility, peptide-based therapeutics are a possible solution. Additionally, in the past two decades, advances in peptide design, delivery, formulation, and devices have occurred, making therapeutic peptides an attractive modality. However, peptide manufacturing is often limited to solid-phase peptide synthesis (SPPS), liquid phase peptide synthesis (LPPS), and to a lesser extent hybrid SPPS/LPPS, with SPPS emerging as a predominant platform technology for peptide synthesis. SPPS involves the use of excess solvents and reagents which negatively impact the environment, thus highlighting the need for newer technologies to reduce the environmental footprint. Herein, fourteen American Chemical Society Green Chemistry Institute Pharmaceutical Roundtable (ACS GCIPR) member companies with peptide-based therapeutics in their portfolio have compiled Process Mass Intensity (PMI) metrics to help inform the sustainability efforts in peptide synthesis. This includes PMI assessment on 40 synthetic peptide processes at various development stages in pharma, classified according to the development phase. This is the most comprehensive assessment of synthetic peptide environmental metrics to date. The synthetic peptide manufacturing process was divided into stages (synthesis, purification, isolation) to determine their respective PMI. On average, solid-phase peptide synthesis (SPPS) (PMI ≈ 13,000) does not compare favorably with other modalities such as small molecules (PMI median 168-308) and biopharmaceuticals (PMI ≈ 8300). Thus, the high PMI for peptide synthesis warrants more environmentally friendly processes in peptide manufacturing.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?

The use of quantum mechanics (QM) has long been the norm to study covalent-binding phenomena in chemistry and biochemistry. The pharmaceutical industry leverages QM models explicitly in covalent drug discovery and implicitly to characterize short-range interactions in noncovalent binding. Predictive toxicology has resisted widespread adoption of QM, including in the pharmaceutical industry, despite its obvious relevance to the metabolic processes in the upstream of adverse outcome pathways and advances in both QM methods and computational resources, which support fit-for-purpose applications in reasonable timeframes. Here, we make the case for embracing QM as an indispensable part of a toxicologist's toolkit. We argue that QM provides the necessary orthogonality to alert-based expert systems and traditional QSARs, consistent with calls for animal-free integrated testing strategies for safety assessments of commercial chemicals. We outline existing roadblocks to this transition, including the need to train model developers in QM and the shift toward service-based toxicity models that utilize high-performance computing clusters. Lastly, we describe recent examples of successful implementations of QM in hazard assessments and propose how in silico toxicology can be further advanced by integrating QM with artificial intelligence.

Identification and characterization of forced degradation products of 5-hydroxymethyl-2-furaldehyde (5-HMF) by HPLC, LC-LTQ/Orbitrap and NMR studies

5-Hydroxymethyl-2-furaldehyde (5-HMF) is a kind of aldehyde compound with highly active furan ring, which is generated by dehydration of glucose, fructose, and other monosaccharides. It widely exists in drugs, foods, health products, cosmetics, and traditional Chinese medicine preparations with high sugar content. Due to the toxicity, the concentration of 5-HMF was always monitored to identify non-conformities and adulteration, as well as ensure the process efficiency, traceability and safety in foods or drugs in the pharmacopoeias of various countries. Herein, a comprehensive forced degradation study was performed to characterize the degradation products (DPs) of 5-HMF under hydrolytic (neutral, acidic, and alkaline) degradation, oxidative, thermal, humidity, and photolytic degradation conditions. A total of five degradants were identified, and two of them (DP-3 and DP-5) were novel DPs first reported in our study. Major DPs (i.e., DP-1 and DP-2) with relatively high peak areas were isolated using semi-preparative HPLC and characterized by LC-LTQ/Orbitrap and NMR. 5-HMF was only stable in alkaline hydrolysis condition. In addition, the degradation pathways and mechanism of these DPs were also explained using LC-LTQ/Orbitrap. In silico toxicity and metabolism behavior of the DPs were evaluated using Derek Nexus and Meteor Nexus software, respectively. The predicted toxicity data indicated that both the drug 5-HMF and its DPs bear the potential of hepatotoxicity, mutagenicity, chromosome damage, and skin sensitisation. Our research may be beneficial for the quality control and suitable storage conditions of 5-HMF.

Direct formation of amide/peptide bonds from carboxylic acids: no traditional coupling reagents, 1-pot, and green

Technology for generating especially important amide and peptide bonds from carboxylic acids and amines that avoids traditional coupling reagents is described. The 1-pot processes developed rely on thioester formation, neat, using a simple dithiocarbamate, and are safe and green, and rely on Nature-inspired thioesters that are then converted to the targeted functionality.

Quantum-Mechanical Approach to Predicting the Carcinogenic Potency of N-Nitroso Impurities in Pharmaceuticals

N-Nitroso contaminants in medicinal products are of concern due to their high carcinogenic potency; however, not all these compounds are created equal, and some are relatively benign chemicals. Understanding the structure-activity relationships (SARs) that drive hazards in one molecule versus another is key to both protecting human health and alleviating costly and sometimes inaccurate animal testing. Here, we report on an extension of the CADRE (computer-aided discovery and REdesign) platform, which is used broadly by the pharmaceutical and personal care industries to assess environmental and human health endpoints, to predict the carcinogenic potency of N-nitroso compounds. The model distinguishes compounds in three potency categories with 77% accuracy in external testing, which surpasses the reproducibility of rodent cancer bioassays and constraints imposed by limited (high-quality) data. The robustness of predictions for more complex pharmaceuticals is maximized by capturing key SARs using quantum mechanics, that is, by hinging the model on the underlying chemistry versus chemicals in the training set. To this end, the present approach can be leveraged in a quantitative hazard assessment and to offer qualitative guidance using electronic structure comparisons between well-studied analogues and unknown contaminants.

Enhancing global and local decision making for chemical safety assessments through increasing the availability of data

Toxicity safety assessments are a fundamental part of the lifecycle of products and aim to protect human health and the environment from harmful exposures to chemical substances. To make decisions regarding the suitability of testing strategies, the applicability of individual tests or concluding an assessment for an individual chemical requires data. This review outlines how different forms of data sharing, from enhancing publicly-available data to extracting knowledge from commercially-sensitive data, leads to increased quantity and quality of evidence being available for safety assessors to review. This can result in more confident decisions for different use cases in the context of chemical safety assessments. Although a number of challenges remain with progressing the evolution of toxicity safety assessments, data sharing should be considered as a key approach to accelerating the development and uptake of new best practices.

Computer-Aided Discovery and Redesign for Respiratory Sensitization: A Tiered Mechanistic Model to Deliver Robust Performance Across a Diverse Chemical Space

Asthma is among the most common occupational diseases with considerable public health and economic costs. Chemicals that induce hypersensitivity in the airways can cause respiratory distress and comorbidities with respiratory infections such as COVID. Robust predictive models for this end point are still elusive due to the lack of an experimental benchmark and the over-reliance of existing in silico tools on structural alerts and structural (vs chemical) similarities. The Computer-Aided Discovery and REdesign (CADRE) platform is a proven strategy for providing robust computational predictions for hazard end points using a tiered hybrid system of expert rules, molecular simulations, and quantum mechanics calculations. The recently developed CADRE model for respiratory sensitization is based on a highly curated data set of structurally diverse chemicals with high-fidelity biological data. The model evaluates absorption kinetics in lung mucosa using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines subsequent reactivity with cell proteins via quantum-mechanics calculations using a multi-tiered regression. The model affords an accuracy above 0.90, with a series of external validations based on literature data in the range of 0.88-0.95. The model is applicable to all low-molecular-weight organics and can inform not only chemical substitution but also chemical redesign to advance development of safer alternatives.

Facile Amide Bond Formation with TCFH-NMI in an Organic Laboratory Course

A new undergraduate organic laboratory experiment has been developed for amide bond formation between biorenewable 2-furoic acid and either of two substituted piperazines to prepare medicinally relevant amide products using a procedure with industrial significance. The reactions proceeded smoothly under ambient conditions using the combination of N,N,N',N'-tetramethylchloroformamidinium hexafluorophosphate (TCFH) and N-methylimidazole (NMI) in a minimal volume of acetonitrile with a direct crystallization upon addition of water. Students successfully collected their product by filtration and then characterized it by NMR (¹H, ¹³C, COSY, DEPT-135, HSQC), IR, MS, and melting point. Students also explored the reaction mechanism and compared green chemistry aspects of their procedure with literature routes. A virtual version of the experiment was adapted for remote instruction.