Mechanisms of Nitrosamine Mutagenicity and Their Relationship to Rodent Carcinogenic Potency.

A thorough literature review was undertaken to understand how the pathways of N-nitrosamine transformation relate to mutagenic potential and carcinogenic potency in rodents. Empirical and computational evidence indicates that a common radical intermediate is created by CYP-mediated hydrogen abstraction at the α-carbon; it is responsible for both activation, leading to the formation of DNA-reactive diazonium species, and deactivation by denitrosation. There are competing sites of CYP metabolism (e.g., ß-carbon), and other reactive species can form following initial bioactivation, although these alternative pathways tend to decrease rather than enhance carcinogenic potency. The activation pathway, oxidative dealkylation, is a common reaction in drug metabolism and evidence indicates that the carbonyl byproduct, e.g., formaldehyde, does not contribute to the toxic properties of N-nitrosamines. Nitric oxide (NO), a side product of denitrosation, can similarly be discounted as an enhancer of N-nitrosamine toxicity based on carcinogenicity data for substances that act as NO-donors. However, not all N-nitrosamines are potent rodent carcinogens. In a significant number of cases, there is a potency overlap with non-N-nitrosamine carcinogens that are not in the Cohort of Concern (CoC; high-potency rodent carcinogens comprising aflatoxin-like-, N-nitroso-, and alkyl-azoxy compounds), while other N-nitrosamines are devoid of carcinogenic potential. In this context, mutagenicity is a useful surrogate for carcinogenicity, as proposed in the ICH M7 (R2) (2023) guidance. Thus, in the safety assessment and control of N-nitrosamines in medicines, it is important to understand those complementary attributes of mechanisms of mutagenicity and structure-activity relationships that translate to elevated potency versus those which are associated with a reduction in, or absence of, carcinogenic potency.

Revisiting the Landscape of Potential Small and Drug Substance Related Nitrosamines in Pharmaceuticals.

N-Nitrosamines are a class of indirect acting mutagens, as their metabolic degradation leads to the formation of the DNA-alkylating diazonium ion. Following up on the in-silico identification of thousands of nitrosamines that can potentially be derived from small molecule drugs and their known impurities described in a previous publication, we have now re-analyzed this dataset to apply EMA's Carcinogenic Potency Categorization Approach (CPCA) introduced with the 16th revision of their Q&A document for Marketing Authorization Holders. We find that the majority of potential nitrosamines from secondary amine precursors belongs to potency categories 4 and 5, corresponding to an acceptable daily intake of 1500 ng, whereas nitrosamines from tertiary amine precursors distribute more evenly among all categories, resulting in a substantial number of structures that are assigned the more challenging acceptable intakes of 18 ng/day and 100 ng/day for potency categories 1 and 2, respectively. However, the nitrosative dealkylation pathway for tertiary amine is generally far slower than the direct nitrosation on secondary amines, with a direct nitrosation mechanism suspected only for structures featuring electron-rich (hetero)aromatic substituents. This allows for greater focus towards those structures that require further review, and we demonstrate that their number is not substantial. In addition, we reflect on the nitrosamine risk posed by secondary amine API impurities and demonstrate that based on the ICH Q3A/B identification threshold unknown impurities may exist that could be transformed to relevant amounts of NA. We also demonstrate that the analytical sensitivity required for the quantification of high potency nitrosamines can be problematic especially for high dose APIs. In summary, the regulatory framework rolled out with the latest Q&A document represents a substantial improvement compared with the previous situation, but further refinement through interaction between manufacturers, regulators, not-for-profit and academic institutions will be required to ensure patient access to vital medicines without compromising safety.

Targeting RNA with small molecules-A safety perspective.

RNA is a major player in cellular function, and consequently can drive a number of disease pathologies. Over the past several years, small molecule-RNA targeting (smRNA targeting) has developed into a promising drug discovery approach. Numerous techniques, tools, and assays have been developed to support this field, and significant investments have been made by pharmaceutical and biotechnology companies. To date, the focus has been on identifying disease validated primary targets for smRNA drug development, yet RNA as a secondary (off) target for all small molecule drug programs largely has been unexplored. In this perspective, we discuss structure, target, and mechanism-driven safety aspects of smRNAs and highlight how these parameters can be evaluated in drug discovery programs to produce potentially safer drugs.

Retrospective analysis of the potential use of virtual control groups in preclinical toxicity assessment using the eTOX database.

Virtual Control Groups (VCGs) based on Historical Control Data (HCD) in preclinical toxicity testing have the potential to reduce animal usage. As a case study we retrospectively analyzed the impact of replacing Concurrent Control Groups (CCGs) with VCGs on the treatment-relatedness of 28 selected histopathological findings reported in either rat or dog in the eTOX database. We developed a novel methodology whereby statistical predictions of treatment-relatedness using either CCGs or VCGs of varying covariate similarity to CCGs were compared to designations from original toxicologist reports; and changes in agreement were used to quantify changes in study outcomes. Generally, the best agreement was achieved when CCGs were replaced with VCGs with the highest level of similarity; the same species, strain, sex, administration route, and vehicle. For example, balanced accuracies for rat findings were 0.704 (predictions based on CCGs) vs. 0.702 (predictions based on VCGs). Moreover, we identified covariates which resulted in poorer identification of treatment-relatedness. This was related to an increasing incidence rate divergence in HCD relative to CCGs. Future databases which collect data at the individual animal level including study details such as animal age and testing facility are required to build adequate VCGs to accurately identify treatment-related effects.

Statistical analysis of preclinical inter-species concordance of histopathological findings in the eTOX database.

Preclinical inter-species concordance can increase the predictivity of observations to the clinic, potentially reducing drug attrition caused by unforeseen adverse events. We quantified inter-species concordance of histopathological findings and target organ toxicities across four preclinical species in the eTOX database using likelihood ratios (LRs). This was done whilst only comparing findings between studies with similar compound exposure (Δ|Cmax| ≤ 1 log-unit), repeat-dosing duration, and animals of the same sex. We discovered 24 previously unreported significant inter-species associations between histopathological findings encoded by the HPATH ontology. More associations with strong positive concordance (33% LR+ > 10) relative to strong negative concordance (12.5% LR- < 0.1) were identified. Of the top 10 most positively concordant associations, 60% were computed between different histopathological findings indicating potential differences in inter-species pathogenesis. We also observed low inter-species target organ toxicity concordance. For example, liver toxicity concordance in short-term studies between female rats and dogs observed an average LR+ of 1.84, and an average LR- of 0.73. This was corroborated by similarly low concordance between rodents and non-rodents for 75 candidate drugs in AstraZeneca. This work provides new statistically significant associations between preclinical species, but finds that concordance is rare, particularly between the absence of findings.

Prediction of inotropic effect based on calcium transients in human iPSC-derived cardiomyocytes and machine learning.

Functional changes to cardiomyocytes are undesirable during drug discovery and identifying the inotropic effects of compounds is hence necessary to decrease the risk of cardiovascular adverse effects in the clinic. Recently, approaches leveraging calcium transients in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have been developed to detect contractility changes, induced by a variety of mechanisms early during drug discovery projects. Although these approaches have been able to provide some predictive ability, we hypothesised that using additional waveform parameters could offer improved insights, as well as predictivity. In this study, we derived 25 parameters from each calcium transient waveform and developed a modified Random Forest method to predict the inotropic effects of the compounds. In total annotated data for 48 compounds were available for modelling, out of which 31 were inotropes. The results show that the Random Forest model with a modified purity criterion performed slightly better than an unmodified algorithm in terms of the Area Under the Curve, giving values of 0.84 vs 0.81 in a cross-validation, and outperformed the ToxCast Pipeline model, for which the highest value was 0.76 when using the best-performing parameter, PW10. Our study hence demonstrates that more advanced parameters derived from waveforms, in combination with additional machine learning methods, provide improved predictivity of cardiovascular risk associated with inotropic effects.

The Landscape of Potential Small and Drug Substance Related Nitrosamines in Pharmaceuticals.

This article reports the outcome of an in silico analysis of more than 12,000 small molecule drugs and drug impurities, identifying the nitrosatable structures, assessing their potential to form nitrosamines under relevant conditions and the challenges to determine compound-specific AIs based on data available or read-across approaches for these nitrosamines and their acceptance by health authorities. Our data indicate that the presence of nitrosamines in pharmaceuticals is likely more prevalent than originally expected. In total, 40.4 % of the analyzed APIs and 29.6 % of the API impurities are potential nitrosamine precursors. Most structures identified through our workflow could form complex API-related nitrosamines, so-called nitrosamine drug substance related impurities (NDSRIs), although we also found structures that could release the well-known small and potent nitrosamines NDMA, NDEA, and others. Due to common structural motifs including secondary or tertiary amine moieties, whole essential drug classes such as beta blockers and ACE inhibitors are at risk. To avoid the risk of drug shortages or even the complete loss of therapeutic options, it will be essential that the well-established ICH M7 principles remain applicable for nitrosamines and that that the industry and regulatory authorities keep an open communication not only about the science but also to make sure there is a good balance between risk and benefit to patients.

Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure.

Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in animals provide a degree of confidence for extrapolation to humans. In addition, prediction of in vivo properties can be used to improve design during drug discovery, help select compounds with better properties, and reduce the number of in vivo experiments. In this study, we generated machine learning models able to predict rat in vivo PK parameters and concentration-time PK profiles based on the molecular chemical structure and either measured or predicted in vitro parameters. The models were trained on internal in vivo rat PK data for over 3000 diverse compounds from multiple projects and therapeutic areas, and the predicted endpoints include clearance and oral bioavailability. We compared the performance of various traditional machine learning algorithms and deep learning approaches, including graph convolutional neural networks. The best models for PK parameters achieved R2 = 0.63 [root mean squared error (RMSE) = 0.26] for clearance and R2 = 0.55 (RMSE = 0.46) for bioavailability. The models provide a fast and cost-efficient way to guide the design of molecules with optimal PK profiles, to enable the prediction of virtual compounds at the point of design, and to drive prioritization of compounds for in vivo assays.

Targeting Xist with compounds that disrupt RNA structure and X inactivation.

Although more than 98% of the human genome is non-coding1, nearly all of the drugs on the market target one of about 700 disease-related proteins. The historical reluctance to invest in non-coding RNA stems partly from requirements for drug targets to adopt a single stable conformation2. Most RNAs can adopt several conformations of similar stabilities. RNA structures also remain challenging to determine3. Nonetheless, an increasing number of diseases are now being attributed to non-coding RNA4 and the ability to target them would vastly expand the chemical space for drug development. Here we devise a screening strategy and identify small molecules that bind the non-coding RNA prototype Xist5. The X1 compound has drug-like properties and binds specifically the RepA motif6 of Xist in vitro and in vivo. Small-angle X-ray scattering analysis reveals that RepA can adopt multiple conformations but favours one structure in solution. X1 binding reduces the conformational space of RepA, displaces cognate interacting protein factors (PRC2 and SPEN), suppresses histone H3K27 trimethylation, and blocks initiation of X-chromosome inactivation. X1 inhibits cell differentiation and growth in a female-specific manner. Thus, RNA can be systematically targeted by drug-like compounds that disrupt RNA structure and epigenetic function.

Deriving waveform parameters from calcium transients in human iPSC-derived cardiomyocytes to predict cardiac activity with machine learning.

Human induced pluripotent stem cell-derived cardiomyocytes have been established to detect dynamic calcium transients by fast kinetic fluorescence assays that provide insights into specific aspects of clinical cardiac activity. However, the precise derivation and use of waveform parameters to predict cardiac activity merit deeper investigation. In this study, we derived, evaluated, and applied 38 waveform parameters in a novel Python framework, including (among others) peak frequency, peak amplitude, peak widths, and a novel parameter, shoulder-tail ratio. We then trained a random forest model to predict cardiac activity based on the 25 parameters selected by correlation analysis. The area under the curve (AUC) obtained for leave-one-compound-out cross-validation was 0.86, thereby replicating the predictions of conventional methods and outperforming fingerprint-based methods by a large margin. This work demonstrates that machine learning is able to automate the assessment of cardiovascular liability from waveform data, reducing any risk of user-to-user variability and bias.

Artificial Intelligence in Drug Safety and Metabolism.

The use of artificial intelligence methods in drug safety began in the early 2000s with applications such as predicting bacterial mutagenicity and hERG inhibition. The field has been endlessly expanding ever since and the models have become more complex. These approaches are now integrated into molecule risk assessment processes along with in vitro and in vivo methods. Today, artificial intelligence can be used in every phase of drug discovery and development, from profiling chemical libraries in early discovery, to predicting off-target effects in the mid-discovery phase, to assessing potential mutagenic impurities in development and degradants as part of life cycle management. This chapter provides an overview of artificial intelligence in drug safety and describes its application throughout the entire discovery and development process.

Optimization of an Imidazo[1,2-a]pyridine Series to Afford Highly Selective Type I1/2 Dual Mer/Axl Kinase Inhibitors with In Vivo Efficacy.

Inhibition of Mer and Axl kinases has been implicated as a potential way to improve the efficacy of current immuno-oncology therapeutics by restoring the innate immune response in the tumor microenvironment. Highly selective dual Mer/Axl kinase inhibitors are required to validate this hypothesis. Starting from hits from a DNA-encoded library screen, we optimized an imidazo[1,2-a]pyridine series using structure-based compound design to improve potency and reduce lipophilicity, resulting in a highly selective in vivo probe compound 32. We demonstrated dose-dependent in vivo efficacy and target engagement in Mer- and Axl-dependent efficacy models using two structurally differentiated and selective dual Mer/Axl inhibitors. Additionally, in vivo efficacy was observed in a preclinical MC38 immuno-oncology model in combination with anti-PD1 antibodies and ionizing radiation.

Promiscuity of in Vitro Secondary Pharmacology Assays and Implications for Lead Optimization Strategies.

We conducted an analysis on screening data generated from 1445 compounds against a panel of 130 enzymes, ion channels, and receptors to assess secondary pharmacological risks. Hit rates of these targets as well as physicochemical properties for those hits were evaluated. A majority of targets yielded hits with higher clogP, molecular weight, and more basic character than inactive compounds. Although most targets favored lipophilic hits, the average clogP of hits at a given target did not correlate with its hit rate. Furthermore, a matched pair analysis was completed to determine structural changes that impacted off-target activities. A correlation of binding assays used in this analysis illustrated that some pharmacologically related binding assays are highly correlative and may be substituted for a smaller set of surrogate assays.

Targeting RNA with Small Molecules: Identification of Selective, RNA-Binding Small Molecules Occupying Drug-Like Chemical Space.

Although the potential value of RNA as a target for new small molecule therapeutics is becoming increasingly credible, the physicochemical properties required for small molecules to selectively bind to RNA remain relatively unexplored. To investigate the druggability of RNAs with small molecules, we have employed affinity mass spectrometry, using the Automated Ligand Identification System (ALIS), to screen 42 RNAs from a variety of RNA classes, each against an array of chemically diverse drug-like small molecules (~50,000 compounds) and functionally annotated tool compounds (~5100 compounds). The set of RNA-small molecule interactions that was generated was compared with that for protein-small molecule interactions, and naïve Bayesian models were constructed to determine the types of specific chemical properties that bias small molecules toward binding to RNA. This set of RNA-selective chemical features was then used to build an RNA-focused set of ~3800 small molecules that demonstrated increased propensity toward binding the RNA target set. In addition, the data provide an overview of the specific physicochemical properties that help to enable binding to potential RNA targets. This work has increased the understanding of the chemical properties that are involved in small molecule binding to RNA, and the methodology used here is generally applicable to RNA-focused drug discovery efforts.

Development of a Platform To Enable Efficient Permeability Evaluation of Novel Organo-Peptide Macrocycles.

As more macrocycle structures are utilized to drug intracellular targets, new platforms are needed to facilitate the discovery of cell permeable compounds in this unique chemical space. Herein, a method is disclosed that allows for the efficient synthesis and permeability evaluation of novel organo-peptide macrocycle libraries. Thoughtful library design allows for the collection of crude permeability data using supercritical fluid chromatography mass spectrometry (SFC-MS) (EPSA) by mass-encoding the stereochemistry, ring size, and organic linker of the desired macrocycles. Library synthesis was aided via the development of a new on-resin N-arylation reaction. Further insights on the permeation of these organo-peptide macrocycles will be discussed, such as the permeability enhancement when utilizing a 2-substituted phenethyl linker versus a 3-substituted phenethyl linker. Lastly, selected macrocycles were scaled up and tested in the MDCK-II permeability assay, and the results of this assay reiterated the permeability trends from the crude SFC-MS data.

Identification of G-Quadruplex-Binding Inhibitors of Myc Expression through Affinity Selection-Mass Spectrometry.

The Myc oncogene is overexpressed in many cancers, yet targeting it for cancer therapy has remained elusive. One strategy for inhibition of Myc expression is through stabilization of the G-quadruplex (G4), a G-rich DNA secondary structure found within the Myc promoter; stabilization of G4s has been shown to halt transcription of downstream gene products. Here we used the Automated Ligand Identification System (ALIS), an affinity selection-mass spectrometry method, to identify compounds that bind to the Myc G4 out of a pool of compounds that had previously been shown to inhibit Myc expression in a reporter screen. Using an ALIS-based screen, we identified hits that bound to the Myc G4, a small subset of which bound preferentially relative to G4s from the promoters of five other genes. To determine functionality and specificity of the Myc G4-binding compounds in cell-based assays, we compared inhibition of Myc expression in cells with and without Myc G4 regulation. Several compounds inhibited Myc expression only in the Myc G4-containing line, and one compound was verified to function through Myc G4 binding. Our study demonstrates that ALIS can be used to identify selective nucleic acid-binding compounds from phenotypic screen hits, increasing the pool of drug targets beyond proteins.

Discovery of Selective RNA-Binding Small Molecules by Affinity-Selection Mass Spectrometry.

Recent advances in understanding the relevance of noncoding RNA (ncRNA) to disease have increased interest in drugging ncRNA with small molecules. The recent discovery of ribocil, a structurally distinct synthetic mimic of the natural ligand of the flavin mononucleotide (FMN) riboswitch, has revealed the potential chemical diversity of small molecules that target ncRNA. Affinity-selection mass spectrometry (AS-MS) is theoretically applicable to high-throughput screening (HTS) of small molecules binding to ncRNA. Here, we report the first application of the Automated Ligand Detection System (ALIS), an indirect AS-MS technique, for the selective detection of small molecule-ncRNA interactions, high-throughput screening against large unbiased small-molecule libraries, and identification and characterization of novel compounds (structurally distinct from both FMN and ribocil) that target the FMN riboswitch. Crystal structures reveal that different compounds induce various conformations of the FMN riboswitch, leading to different activity profiles. Our findings validate the ALIS platform for HTS screening for RNA-binding small molecules and further demonstrate that ncRNA can be broadly targeted by chemically diverse yet selective small molecules as therapeutics.

RNA as a small molecule druggable target.

Small molecule drugs have readily been developed against many proteins in the human proteome, but RNA has remained an elusive target for drug discovery. Increasingly, we see that RNA, and to a lesser extent DNA elements, show a persistent tertiary structure responsible for many diverse and complex cellular functions. In this digest, we have summarized recent advances in screening approaches for RNA targets and outlined the discovery of novel, drug-like small molecules against RNA targets from various classes and therapeutic areas. The link of structure, function, and small-molecule Druggability validates now for the first time that RNA can be the targets of therapeutic agents.

Identification of quinazoline based inhibitors of IRAK4 for the treatment of inflammation.

Interleukin-1 receptor associated kinase 4 (IRAK4) has been implicated in IL-1R and TLR based signaling. Therefore selective inhibition of the kinase activity of this protein represents an attractive target for the treatment of inflammatory diseases. Medicinal chemistry optimization of high throughput screening (HTS) hits with the help of structure based drug design led to the identification of orally-bioavailable quinazoline based IRAK4 inhibitors with excellent pharmacokinetic profile and kinase selectivity. These highly selective IRAK4 compounds show activity in vivo via oral dosing in a TLR7 driven model of inflammation.

Synthesis of Cyclic Peptidomimetics via a Pd-Catalyzed Macroamination Reaction.

A new method to access cyclic peptidomimetics via a Pd-catalyzed macroamination reaction is presented. Natural amino acid amines are revealed as proficient coupling partners in these transformations. With a commercially available CPhos G3 catalyst system and substrates bearing diverse amino acid and aryl halide backbones, the unique head to side-chain (or side-chain mimic) macrocycles are afforded with ring sizes from 11 to 23 members in yields up to 84%.