Tight-Binding Small-Molecule Carboxylesterase 2 Inhibitors Reduce Intracellular Irinotecan Activation.

As the primary enzyme responsible for the activatable conversion of Irinotecan (CPT-11) to SN-38, carboxylesterase 2 (CES2) is a significant predictive biomarker toward CPT-11-based treatments for pancreatic ductal adenocarcinoma (PDAC). High SN-38 levels from high CES2 activity lead to harmful effects, including life-threatening diarrhea. While alternate strategies have been explored, CES2 inhibition presents an effective strategy to directly alter the pharmacokinetics of CPT-11 conversion, ultimately controlling the amount of SN-38 produced. To address this, we conducted a high-throughput screening to discover 18 small-molecule CES2 inhibitors. The inhibitors are validated by dose-response and counter-screening and 16 of these inhibitors demonstrate selectivity for CES2. These 16 inhibitors inhibit CES2 in cells, indicating cell permeability, and they show inhibition of CPT-11 conversion with the purified enzyme. The top five inhibitors prohibited cell death mediated by CPT-11 when preincubated in PDAC cells. Three of these inhibitors displayed a tight-binding mechanism of action with a strong binding affinity.

Smart Sensor Control and Monitoring of an Automated Cell Expansion Process.

Immune therapy for cancer patients is a new and promising area that in the future may complement traditional chemotherapy. The cell expansion phase is a critical part of the process chain to produce a large number of high-quality, genetically modified immune cells from an initial sample from the patient. Smart sensors augment the ability of the control and monitoring system of the process to react in real-time to key control parameter variations, adapt to different patient profiles, and optimize the process. The aim of the current work is to develop and calibrate smart sensors for their deployment in a real bioreactor platform, with adaptive control and monitoring for diverse patient/donor cell profiles. A set of contrasting smart sensors has been implemented and tested on automated cell expansion batch runs, which incorporate advanced data-driven machine learning and statistical techniques to detect variations and disturbances of the key system features. Furthermore, a 'consensus' approach is applied to the six smart sensor alerts as a confidence factor which helps the human operator identify significant events that require attention. Initial results show that the smart sensors can effectively model and track the data generated by the Aglaris FACER bioreactor, anticipate events within a 30 min time window, and mitigate perturbations in order to optimize the key performance indicators of cell quantity and quality. In quantitative terms for event detection, the consensus for sensors across batch runs demonstrated good stability: the AI-based smart sensors (Fuzzy and Weighted Aggregation) gave 88% and 86% consensus, respectively, whereas the statistically based (Stability Detector and Bollinger) gave 25% and 42% consensus, respectively, the average consensus for all six being 65%. The different results reflect the different theoretical approaches. Finally, the consensus of batch runs across sensors gave even higher stability, ranging from 57% to 98% with an average consensus of 80%.

Energy Consumption Optimization of a Fluid Bed Dryer in Pharmaceutical Manufacturing Using EDA (Exploratory Data Analysis).

In this paper, a data preprocessing methodology, EDA (Exploratory Data Analysis), is used for performing an exploration of the data captured from the sensors of a fluid bed dryer to reduce the energy consumption during the preheating phase. The objective of this process is the extraction of liquids such as water through the injection of dry and hot air. The time taken to dry a pharmaceutical product is typically uniform, independent of the product weight (Kg) or the type of product. However, the time it takes to heat up the equipment before drying can vary depending on different factors, such as the skill level of the person operating the machine. EDA (Exploratory Data Analysis) is a method of evaluating or comprehending sensor data to derive insights and key characteristics. EDA is a critical component of any data science or machine learning process. The exploration and analysis of the sensor data from experimental trials has facilitated the identification of an optimal configuration, with an average reduction in preheating time of one hour. For each processed batch of 150 kg in the fluid bed dryer, this translates into an energy saving of around 18.5 kWh, giving an annual energy saving of over 3.700 kWh.

Industry Perspective on the Pharmacokinetic and Absorption, Distribution, Metabolism, and Excretion Characterization of Heterobifunctional Protein Degraders.

Targeted protein degraders (TPDs), specifically the bifunctional protein degraders discussed in this manuscript, consist of two linked ligands for a protein of interest and an E3 ligase, resulting in molecules that largely violate accepted physicochemical limits (e.g., Lipinski's Rule of Five) for oral bioavailability. In 2021, the IQ Consortium Degrader DMPK/ADME Working Group undertook a survey of 18 IQ member and nonmember companies working on degraders to understand whether the characterization and optimization of these molecules were different from any other beyond the Rule of Five (bRo5) compounds. Additionally, the working group sought to identify pharmacokinetic (PK)/absorption, distribution, metabolism, and excretion (ADME) areas in need of further evaluation and where additional tools could aid in more rapid advancement of TPDs to patients. The survey revealed that although TPDs reside in a challenging bRo5 physicochemical space, most respondents focus their efforts on oral delivery. Physicochemical properties required for oral bioavailability were generally consistent across the companies surveyed. Many of the member companies used modified assays to address challenging degrader properties (e.g., solubility, nonspecific binding), but only half indicated that they modified their drug discovery workflows. The survey also suggested the need for further scientific investigation in the areas of central nervous system penetration, active transport, renal elimination, lymphatic absorption, in silico/machine learning, and human pharmacokinetic prediction. Based on the survey results, the Degrader DMPK/ADME Working Group concluded that TPD evaluation does not fundamentally differ from other bRo5 compounds but requires some modification compared with traditional small molecules and proposes a generic workflow for PK/ADME evaluation of bifunctional TPDs. SIGNIFICANCE STATEMENT: Based on an industry survey, this article provides an understanding of the current state of absorption, distribution, metabolism, and excretion science pertaining to characterizing and optimizing targeted protein degraders, specifically bifunctional protein degraders, based upon responses by 18 IQ consortium members and non-members developing targeted protein degraders. Additionally, this article puts into context the differences / similarities in methods and strategies utilized for heterobifunctional protein degraders compared to other beyond Rule of Five molecules and conventional small molecule drugs.

Clearance of plasma PCSK9 via the asialoglycoprotein receptor mediated by heterobifunctional ligands.

Proprotein convertase subtilisin/kexin type 9 (PCSK9) regulates plasma low-density lipoprotein cholesterol (LDL-C) levels by promoting the degradation of hepatic LDL receptors (LDLRs). Current therapeutic approaches use antibodies that disrupt PCSK9 binding to LDLR to reduce circulating LDL-C concentrations or siRNA that reduces PCSK9 synthesis and thereby levels in circulation. Recent reports describe small molecules that, like therapeutic antibodies, interfere with PCSK9 binding to LDLR. We report an alternative approach to decrease circulating PCSK9 levels by accelerating PCSK9 clearance and degradation using heterobifunctional molecules that simultaneously bind to PCSK9 and the asialoglycoprotein receptor (ASGPR). Various formats, including bispecific antibodies, antibody-small molecule conjugates, and heterobifunctional small molecules, demonstrate binding in vitro and accelerated PCSK9 clearance in vivo. These molecules showcase a new approach to PCSK9 inhibition, targeted plasma protein degradation (TPPD), and demonstrate the feasibility of heterobifunctional small molecule ligands to accelerate the clearance and degradation of pathogenic proteins in circulation.

Identification of a PCSK9-LDLR disruptor peptide with in vivo function.

Proprotein convertase subtilisin/kexin type 9 (PCSK9) regulates plasma low-density lipoprotein cholesterol (LDL-C) levels by promoting hepatic LDL receptor (LDLR) degradation. Therapeutic antibodies that disrupt PCSK9-LDLR binding reduce LDL-C concentrations and cardiovascular disease risk. The epidermal growth factor precursor homology domain A (EGF-A) of the LDLR serves as a primary contact with PCSK9 via a flat interface, presenting a challenge for identifying small molecule PCSK9-LDLR disruptors. We employ an affinity-based screen of 1013in vitro-translated macrocyclic peptides to identify high-affinity PCSK9 ligands that utilize a unique, induced-fit pocket and partially disrupt the PCSK9-LDLR interaction. Structure-based design led to molecules with enhanced function and pharmacokinetic properties (e.g., 13PCSK9i). In mice, 13PCSK9i reduces plasma cholesterol levels and increases hepatic LDLR density in a dose-dependent manner. 13PCSK9i functions by a unique, allosteric mechanism and is the smallest molecule identified to date with in vivo PCSK9-LDLR disruptor function.

Scalable Biosynthetic Production of Knotted Peptides Enables ADME and Thermodynamic Folding Studies.

Knotted peptides present a wealth of structurally diverse, biologically active molecules, with the inhibitor cystine knot/knottin class among the most ecologically common ones. Many of these natural products interact with extracellular targets such as voltage-gated ion channels with exquisite selectivity and potency, making them intriguing therapeutic modalities. Such compounds are often produced in low concentrations by intractable organisms, making structural and biological characterization challenging, which is frequently overcome by various expression strategies. Here, we sought to test a biosynthetic route for the expression and study of knotted peptides. We screened expression constructs for a biosynthesized knotted peptide to determine the most influential parameters for successful disulfide folding and used NMR spectroscopic fingerprinting to validate topological structures. We performed pharmacokinetic characterization, which indicated that the interlocking disulfide structure minimizes liabilities of linear peptide sequences, and propose a mechanism by which knotted peptides are cleared. We then developed an assay to monitor solution folding in real time, providing a strategy for studying the folding process during maturation, which provided direct evidence for the importance of backbone organization as the driving force for topology formation.

Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling.

In the increasingly pressing context of improving recycling, optical technologies present a broad potential to support the adequate sorting of plastics. Nevertheless, the commercially available solutions (for example, employing near-infrared spectroscopy) generally focus on identifying mono-materials of a few selected types which currently have a market-interest as secondary materials. Current progress in photonic sciences together with advanced data analysis, such as artificial intelligence, enable bridging practical challenges previously not feasible, for example in terms of classifying more complex materials. In the present paper, the different techniques are initially reviewed based on their main characteristics. Then, based on academic literature, their suitability for monitoring the composition of multi-materials, such as different types of multi-layered packaging and fibre-reinforced polymer composites as well as black plastics used in the motor vehicle industry, is discussed. Finally, some commercial systems with applications in those sectors are also presented. This review mainly focuses on the materials identification step (taking place after waste collection and before sorting and reprocessing) but in outlook, further insights on sorting are given as well as future prospects which can contribute to increasing the circularity of the plastic composites' value chains.

Design of Thioether Cyclic Peptide Scaffolds with Passive Permeability and Oral Exposure.

Advances in the design of permeable peptides and in the synthesis of large arrays of macrocyclic peptides with diverse amino acids have evolved on parallel but independent tracks. Less precedent combines their respective attributes, thereby limiting the potential to identify permeable peptide ligands for key targets. Herein, we present novel 6-, 7-, and 8-mer cyclic peptides (MW 774-1076 g·mol-1) with passive permeability and oral exposure that feature the amino acids and thioether ring-closing common to large array formats, including DNA- and RNA-templated synthesis. Each oral peptide herein, selected from virtual libraries of partially N-methylated peptides using in silico methods, reflects the subset consistent with low energy conformations, low desolvation penalties, and passive permeability. We envision that, by retaining the backbone N-methylation pattern and consequent bias toward permeability, one can generate large peptide arrays with sufficient side chain diversity to identify permeability-biased ligands to a variety of protein targets.

Bio-Based Packaging: Materials, Modifications, Industrial Applications and Sustainability.

Environmental impacts and consumer concerns have necessitated the study of bio-based materials as alternatives to petrochemicals for packaging applications. The purpose of this review is to summarize synthetic and non-synthetic materials feasible for packaging and textile applications, routes of upscaling, (industrial) applications, evaluation of sustainability, and end-of-life options. The outlined bio-based materials include polylactic acid, polyethylene furanoate, polybutylene succinate, and non-synthetically produced polymers such as polyhydrodyalkanoate, cellulose, starch, proteins, lipids, and waxes. Further emphasis is placed on modification techniques (coating and surface modification), biocomposites, multilayers, and additives used to adjust properties especially for barriers to gas and moisture and to tune their biodegradability. Overall, this review provides a holistic view of bio-based packaging material including processing, and an evaluation of the sustainability of and options for recycling. Thus, this review contributes to increasing the knowledge of available sustainable bio-based packaging material and enhancing the transfer of scientific results into applications.

Predicting rice blast disease: machine learning versus process-based models.

BACKGROUND: In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and Water Accounting Rice Model (WARM)) and two approaches based on machine learning algorithms (M5Rules and Recurrent Neural Networks (RNN)), the former inducing a rule-based model and the latter building a neural network. In situ telemetry is important to obtain quality in-field data for predictive models and this was a key aspect of the RICE-GUARD project on which this study is based. According to the authors, this is the first time process-based and machine learning modelling approaches for supporting plant disease management are compared. RESULTS: Results clearly showed that the models succeeded in providing a warning of rice blast onset and presence, thus representing suitable solutions for preventive remedial actions targeting the mitigation of yield losses and the reduction of fungicide use. All methods gave significant "signals" during the "early warning" period, with a similar level of performance. M5Rules and WARM gave the maximum average normalized scores of 0.80 and 0.77, respectively, whereas Yoshino gave the best score for one site (Kalochori 2015). The best average values of r and r2 and %MAE (Mean Absolute Error) for the machine learning models were 0.70, 0.50 and 0.75, respectively and for the process-based models the corresponding values were 0.59, 0.40 and 0.82. Thus it has been found that the ML models are competitive with the process-based models. This result has relevant implications for the operational use of the models, since most of the available studies are limited to the analysis of the relationship between the model outputs and the incidence of rice blast. Results also showed that machine learning methods approximated the performances of two process-based models used for years in operational contexts. CONCLUSIONS: Process-based and data-driven models can be used to provide early warnings to anticipate rice blast and detect its presence, thus supporting fungicide applications. Data-driven models derived from machine learning methods are a viable alternative to process-based approaches and - in cases when training datasets are available - offer a potentially greater adaptability to new contexts.

Medicinal Chemistry Profiling of Monocyclic 1,2-Azaborines.

The first examples of biologically active monocyclic 1,2-azaborines have been synthesized and demonstrated to exhibit not only improved in vitro aqueous solubility in comparison with their corresponding carbonaceous analogues, but in the context of a CDK2 inhibitor, also improved biological activity and better in vivo oral bioavailability. This proof-of-concept study establishes the viability of monocyclic 1,2-azaborines as a novel pharmacophore with distinct pharmacological profiles that can help address challenges associated with solubility in drug development research.

Assessment of cytochrome p450 enzyme inhibition and inactivation in drug discovery and development.

Evaluation of the potential of a drug candidate to inhibit or inactivate cytochrome P450 (CYP) enzymes remains an important part of pharmaceutical drug Discovery and Development programs. CYP enzymes are considered to be one of the most important enzyme families involved in the metabolic clearance of the vast majority of prescribed drugs. Clinical drug-drug interactions (DDI) involving inhibition or time-dependent inactivation of these enzymes can result in dangerous side effects resulting from reduced clearance/increased exposure of the drug being affected (the 'victim' drug). In this regard, pharmaceutical companies have become quite vigilant in mitigating CYP inhibition/inactivation liabilities of drug candidates early in Discovery including continued risk assessment throughout Development. In this review, common strategies and decision making processes for the assessment of DDI risk in the different stages of pharmaceutical development are discussed. In addition, in vitro study designs, analysis, and interpretation of CYP inhibition and inactivation data are described in stage appropriate context. The in vitro tools and knowledge available now enable the Discovery Chemist to place the potential CYP DDI liability of a drug candidate into perspective and to aid in the optimization of chemical drug design to further mitigate this risk.

Predicting safety toleration of pharmaceutical chemical leads: cytotoxicity correlations to exploratory toxicity studies.

The selection and application of appropriate safety screening paradigms could revolutionize the drug discovery process by reducing safety-related attrition. While mechanism specific genotoxicity and safety pharmacology assays are routinely used in screening, the overall value of employing nonspecific cytotoxicity assays remains controversial. A retrospective analysis of safety findings from rat exploratory toxicity studies (4-14 days) utilizing compounds that spanned broad therapeutic targets (protease, transport, G-protein-coupled receptors, and kinase inhibitors, cGMP modulators) demonstrated that safety toleration in vivo could be approximated using cytotoxicity values. A composite safety score was calculated for each compound dose based on findings in each of the following categories: systemic toleration (mortality, food consumption, and adverse clinical signs), clinical chemistry/hematology parameters (deviations from normal ranges), and multiorgan pathology (necrosis or incidence/severity of histologic change). Binning compounds into potent (LC(50)<10 microM) and non-potent (LC(50)>100 microM) cytotoxicants in vitro showed that compared to non-potent cytotoxicants the exposure to potent cytotoxicants in vivo resulted in higher overall severity scores at lower exposures. Correlating overall toleration for individual compounds was further refined when in vivo exposure was considered. When average plasma exposure (Cp(ave)) for a compound exceeded its mean lethal concentration (LC(50)) in vitro (Cp(ave)/LC(50)>1), higher overall severity scores were achieved compared to lower exposure margins (Cp(ave)/LC(50) <0.01). Based on this analysis, the ability to select lead series and individual compounds with better safety characteristics is presented. In summary, cytotoxicity screening can be used to approximate, not define, the safety characteristics of lead pharmaceutical series early in the drug discovery process.

A combined model for predicting CYP3A4 clinical net drug-drug interaction based on CYP3A4 inhibition, inactivation, and induction determined in vitro.

Although approaches to the prediction of drug-drug interactions (DDIs) arising via time-dependent inactivation have recently been developed, such approaches do not account for simple competitive inhibition or induction. Accordingly, these approaches do not provide accurate predictions of DDIs arising from simple competitive inhibition (e.g., ketoconazole) or induction of cytochromes P450 (e.g., phenytoin). In addition, methods that focus upon a single interaction mechanism are likely to yield misleading predictions in the face of mixed mechanisms (e.g., ritonavir). As such, we have developed a more comprehensive mathematical model that accounts for the simultaneous influences of competitive inhibition, time-dependent inactivation, and induction of CYP3A in both the liver and intestine to provide a net drug-drug interaction prediction in terms of area under the concentration-time curve ratio. This model provides a framework by which readily obtained in vitro values for competitive inhibition, time-dependent inactivation and induction for the precipitant compound as well as literature values for f(m) and F(G) for the object drug can be used to provide quantitative predictions of DDIs. Using this model, DDIs arising via inactivation (e.g., erythromycin) continue to be well predicted, whereas those arising via competitive inhibition (e.g., ketoconazole), induction (e.g., phenytoin), and mixed mechanisms (e.g., ritonavir) are also predicted within the ranges reported in the clinic. This comprehensive model quantitatively predicts clinical observations with reasonable accuracy and can be a valuable tool to evaluate candidate drugs and rationalize clinical DDIs.

Fluorescently labeled analogues of dofetilide as high-affinity fluorescence polarization ligands for the human ether-a-go-go-related gene (hERG) channel.

Novel fluorescent derivatives of dofetilide (1) have been synthesized. Analogues that feature a fluorescent probe attached through an aliphatic spacer to the central tertiary nitrogen of 1 have high affinity for the hERG channel, and affinity is dependent on both linker length and pendent dye. These variables have been optimized to generate Cy3B derivative 10e, which has hERG channel affinity equivalent to that of dofetilide. When bound to cell membranes expressing the hERG channel, 10e shows a robust increase in fluorescence polarization (FP) signal. In a FP binding assay using 10e as tracer ligand, Ki values for several known hERG channel blockers were measured and excellent agreement with the literature Ki values was observed over an affinity range of 2 nM to 3 muM. 10e blocks hERG channel current in electrophysiological patch clamp experiments, and computational docking experiments predict that the dofetilide core of 10e binds hERG channel in a conformation similar to that previously predicted for 1. These analogues enable high-throughput hERG channel binding assays that are rapid, economical, and predictive of test compounds' potential for prolonged QT liabilities.

Glucose-induced conformational changes in glucokinase mediate allosteric regulation: transient kinetic analysis.

The transient kinetics of glucose binding to glucokinase (GK) was studied using stopped-flow fluorescence spectrophotometry to investigate the underlying mechanism of positive cooperativity of monomeric GK with glucose. Glucose binding to GK was shown to display biphasic kinetics that fit best to a reversible two-step mechanism. GK initially binds glucose to form a transient intermediate, namely, E* x glucose, followed by a conformational change to a catalytically competent E x glucose complex. The microscopic rate constants for each step were determined as follows: on rate k1 of 557 M(-1) s(-1) and off rate k(-1) of 8.1 s(-1) for E* x glucose formation, and forward rate k2 of 0.45 s(-1) and reverse rate k(-2) of 0.28 s(-1) for the conformational change from E* x glucose to E x glucose. These results suggest that the enzyme conformational change induced by glucose binding is a reversible, slow event that occurs outside the catalytic cycle (kcat = 38 s(-1)). This slow transition between the two enzyme conformations modulated by glucose likely forms the kinetic foundation for the allosteric regulation. Furthermore, the kinetics of the enzyme conformational change was altered in favor of E x glucose formation in D2O, accompanied by a decrease in cooperativity with glucose (Hill slope of 1.3 in D2O vs 1.7 in H2O). The deuterium solvent isotope effects confirm the role of the conformational change in the magnitude of glucose cooperativity. Similar studies were conducted with GK activating mutation Y214C at the allosteric activator site that is likely involved in the protein domain rearrangement associated with glucose binding. The mutation enhanced equilibrium glucose binding by a combination of effects on both the formation of E* x glucose and an enzyme conformational change to E x glucose. Kinetic simulation by KINSIM supports the conclusion that the kinetic cooperativity of GK arises from slow glucose-induced conformational changes in GK.

A new chemical tool for exploring the role of the PDE4D isozyme in leukocyte function.

Nicotinamide (2) is a potent and selective inhibitor of the PDE4D isozyme and as a chemical tool selectively blocks eosinophil mediator release and chemotaxis thus linking the role of PDE4D to eosinophil function.

Simple strategies for reducing sample loads in in vitro metabolic stability high-throughput screening experiments: a comparison between traditional, two-time-point and pooled sample analyses.

Higher-throughput ADME programs in early drug discovery are becoming common throughout the pharmaceutical industry as companies strive to reduce their compound attrition in later-stage development. Many of the ADME assays developed into higher-throughput formats rely on LC/MS analyses. Since the biological aspects of the assay are amenable to parallel processes using dense plate formats, the number of samples generated from these assays produce a large analysis load for serial LC/MS. Presented in this report are two novel strategies, including a sample pooling method and a two time-point method, that could be used in drug discovery to reduce the number of samples generated during multiple time-point in-vitro ADME assays. One hundred and sixty-three compounds were subjected to human microsomal incubations with full time-point method samples taken at t = 0, 5, 15, 30, and 45 min. The ER data correlation (R(2)) between the full time-point method and the pooling method and two time-point methods were 0.98 and 0.97, respectively. Both methods have the potential to: 1. produce data of similar quality to traditional high throughput ADME assays, 2. be easily implemented, 3. shorten analytical run times, and 4. be reproducible and robust.