Mathematical and Structural Characterization of Strong Nonadditive Structure-Activity Relationship Caused by Protein Conformational Changes

From mundane to surprising nonadditivity: drivers and impact on ML models

Nonadditivity (NA) in Structure-Activity and Structure-Property Relationship (SAR) data is a rare but very information rich phenomenon. It can indicate conformational flexibility, structural rearrangements, and errors in assay results and structural assignment. While purely ligand-based conformational causes of NA are rather well understood and mundane, other factors are less so and cause surprising NA that has a huge influence on SAR analysis and ML model performance. We here report a systematic analysis across a wide range of properties (20 on-target biological activities and 4 physicochemical ADME-related properties) to understand the frequency of various different phenomena that may lead to NA. A set of novel descriptors were developed to characterize double transformation cycles and identify trends in NA. Double transformation cycles were classified into "surprising" and "mundane" categories, with the majority being classed as mundane. We also examined commonalities among surprising cycles, finding LogP differences to have the most significant impact on NA. A distinct behavior of NA for on-target sets compared to ADME sets was observed. Finally, we show that machine learning models struggle with highly nonadditive data, indicating that a better understanding of NA is an important future research direction.

3-Arylidene-2-oxindoles as GSK3β inhibitors and anti-thrombotic agents

Development of novel agents that prevent thrombotic events is an urgent task considering increasing incidence of cardiovascular diseases and coagulopathies that accompany cancer and COVID-19. Enzymatic assay identified novel GSK3β inhibitors in a series of 3-arylidene-2-oxindole derivatives. Considering the putative role of GSK3β in platelet activation, the most active compounds were evaluated for antiplatelet activity and antithrombotic activity. It was found that GSK3β inhibition by 2-oxindoles correlates with inhibition of platelet activation only for compounds 1b and 5a. Albeit, in vitro antiplatelet activity matched well with in vivo anti-thrombosis activity. The most active GSK3β inhibitor 5a exceeds antiplatelet activity of acetylsalicylic acid in vitro by 10.3 times and antithrombotic activity in vivo by 18.7 times (ED₅₀ 7.3 mg/kg). These results support the promising role of GSK3β inhibitors for development of novel antithrombotic agents.

Implications of Additivity and Nonadditivity for Machine Learning and Deep Learning Models in Drug Design

Matched molecular pairs (MMPs) are nowadays a commonly applied concept in drug design. They are used in many computational tools for structure-activity relationship analysis, biological activity prediction, or optimization of physicochemical properties. However, until now it has not been shown in a rigorous way that MMPs, that is, changing only one substituent between two molecules, can be predicted with higher accuracy and precision in contrast to any other chemical compound pair. It is expected that any model should be able to predict such a defined change with high accuracy and reasonable precision. In this study, we examine the predictability of four classical properties relevant for drug design ranging from simple physicochemical parameters (log D and solubility) to more complex cell-based ones (permeability and clearance), using different data sets and machine learning algorithms. Our study confirms that additive data are the easiest to predict, which highlights the importance of recognition of nonadditivity events and the challenging complexity of predicting properties in case of scaffold hopping. Despite deep learning being well suited to model nonlinear events, these methods do not seem to be an exception of this observation. Though they are in general performing better than classical machine learning methods, this leaves the field with a still standing challenge.

Nonadditivity in public and inhouse data: implications for drug design

Numerous ligand-based drug discovery projects are based on structure-activity relationship (SAR) analysis, such as Free-Wilson (FW) or matched molecular pair (MMP) analysis. Intrinsically they assume linearity and additivity of substituent contributions. These techniques are challenged by nonadditivity (NA) in protein-ligand binding where the change of two functional groups in one molecule results in much higher or lower activity than expected from the respective single changes. Identifying nonlinear cases and possible underlying explanations is crucial for a drug design project since it might influence which lead to follow. By systematically analyzing all AstraZeneca (AZ) inhouse compound data and publicly available ChEMBL25 bioactivity data, we show significant NA events in almost every second assay among the inhouse and once in every third assay in public data sets. Furthermore, 9.4% of all compounds of the AZ database and 5.1% from public sources display significant additivity shifts indicating important SAR features or fundamental measurement errors. Using NA data in combination with machine learning showed that nonadditive data is challenging to predict and even the addition of nonadditive data into training did not result in an increase in predictivity. Overall, NA analysis should be applied on a regular basis in many areas of computational chemistry and can further improve rational drug design.

Design, Synthesis, and Biological Evaluation of Novel 6H-Benzo[c]chromen-6-one Derivatives as Potential Phosphodiesterase II Inhibitors

Urolithins (hydroxylated 6H-benzo[c]chromen-6-ones) are the main bioavailable metabolites of ellagic acid (EA), which was shown to be a cognitive enhancer in the treatment of neurodegenerative diseases. As part of this research, a series of alkoxylated 6H-benzo[c]chromen-6-one derivatives were designed and synthesized. Furthermore, their biological activities were evaluated as potential PDE2 inhibitors, and the alkoxylated 6H-benzo[c]chromen-6-one derivative 1f was found to have the optimal inhibitory potential (IC₅₀: 3.67 ± 0.47 μM). It also exhibited comparable activity in comparison to that of BAY 60-7550 in vitro cell level studies.

Radiosynthesis and Biological Investigation of a Novel Fluorine-18 Labeled Benzoimidazotriazine- Based Radioligand for the Imaging of Phosphodiesterase 2A with Positron Emission Tomography

A specific radioligand for the imaging of cyclic nucleotide phosphodiesterase 2A (PDE2A) via positron emission tomography (PET) would be helpful for research on the physiology and disease-related changes in the expression of this enzyme in the brain. In this report, the radiosynthesis of a novel PDE2A radioligand and the subsequent biological evaluation were described. Our prospective compound 1-(2-chloro-5-methoxy phenyl)-8-(2-fluoropyridin-4-yl)-3- methylbenzo[e]imidazo[5,1-c][1,2,4]triazine, benzoimidazotriazine (BIT1) (IC₅₀ PDE2A = 3.33 nM; 16-fold selectivity over PDE10A) was fluorine-18 labeled via aromatic nucleophilic substitution of the corresponding nitro precursor using the K[¹⁸F]F-K_2.2.2-carbonate complex system. The new radioligand [¹⁸F]BIT1 was obtained with a high radiochemical yield (54 ± 2%, n = 3), a high radiochemical purity (≥99%), and high molar activities (155–175 GBq/μmol, n = 3). In vitro autoradiography on pig brain cryosections exhibited a heterogeneous spatial distribution of [¹⁸F]BIT1 corresponding to the known pattern of expression of PDE2A. The investigation of in vivo metabolism of [¹⁸F]BIT1 in a mouse revealed sufficient metabolic stability. PET studies in mouse exhibited a moderate brain uptake of [¹⁸F]BIT1 with a maximum standardized uptake value of ~0.7 at 5 min p.i. However, in vivo blocking studies revealed a non-target specific binding of [¹⁸F]BIT1. Therefore, further structural modifications are needed to improve target selectivity.

Nonadditivity Analysis

We introduce the statistics behind a novel type of SAR analysis named "nonadditivity analysis". On the basis of all pairs of matched pairs within a given data set, the approach analyzes whether the same transformations between related molecules have the same effect, i.e., whether they are additive. Assuming that the experimental uncertainty is normally distributed, the additivities can be analyzed with statistical rigor and sets of compounds can be found that show significant nonadditivity. Nonadditivity analysis can not only detect nonadditivity, potential SAR outliers, and sets of key compounds but also allow estimating an upper limit of the experimental uncertainty in the data set. We demonstrate how complex SAR features that inform medicinal chemistry can be found in large SAR data sets. Finally, we show how the upper limit of experimental uncertainty for a given biochemical assay can be estimated without the need for repeated measurements of the same protein-ligand system.

Synthesis and In Vitro Evaluation of 8-Pyridinyl-Substituted Benzo[e]imidazo[2,1-c][1,2,4]triazines as Phosphodiesterase 2A Inhibitors

Phosphodiesterase 2A (PDE2A) is highly expressed in distinct areas of the brain, which are known to be related to neuropsychiatric diseases. The development of suitable PDE2A tracers for Positron Emission Tomography (PET) would permit the in vivo imaging of the PDE2A and evaluation of disease-mediated alterations of its expression. A series of novel fluorinated PDE2A inhibitors on the basis of a Benzoimidazotriazine (BIT) scaffold was prepared leading to a prospective inhibitor for further development of a PDE2A PET imaging agent. BIT derivatives (BIT1–9) were obtained by a seven-step synthesis route, and their inhibitory potency towards PDE2A and selectivity over other PDEs were evaluated. BIT1 demonstrated much higher inhibition than other BIT derivatives (82.9% inhibition of PDE2A at 10 nM). BIT1 displayed an IC₅₀ for PDE2A of 3.33 nM with 16-fold selectivity over PDE10A. This finding revealed that a derivative bearing both a 2-fluoro-pyridin-4-yl and 2-chloro-5-methoxy-phenyl unit at the 8- and 1-position, respectively, appeared to be the most potent inhibitor. In vitro studies of BIT1 using mouse liver microsomes (MLM) disclosed BIT1 as a suitable ligand for ¹⁸F-labeling. Nevertheless, future in vivo metabolism studies are required.

Decision Making in Medicinal Chemistry: The Power of Our Intuition

Medicinal chemists rely on their intuition to make decisions regarding the course of a medicinal chemistry program. Our ability to accurately and efficiently process large data sets routinely requires that we reduce the volume of information to manageable proportions. This prioritization process, however, can be affected by intuitive biases. One such situation is structure-activity relationship (SAR) analysis in nonadditive data sets in which attempts to intuitively predict the activity of compounds based on preliminary data can lead to erroneous conclusions. Matrix analysis can be a useful tool to accurately determine the nature of the SAR and to improve our decision-making process during an analoging campaign.