Deleterious, protein-altering variants in the transcriptional coregulator ZMYM3 in 27 individuals with a neurodevelopmental delay phenotype.

Neurodevelopmental disorders (NDDs) result from highly penetrant variation in hundreds of different genes, some of which have not yet been identified. Using the MatchMaker Exchange, we assembled a cohort of 27 individuals with rare, protein-altering variation in the transcriptional coregulator ZMYM3, located on the X chromosome. Most (n = 24) individuals were males, 17 of which have a maternally inherited variant; six individuals (4 male, 2 female) harbor de novo variants. Overlapping features included developmental delay, intellectual disability, behavioral abnormalities, and a specific facial gestalt in a subset of males. Variants in almost all individuals (n = 26) are missense, including six that recurrently affect two residues. Four unrelated probands were identified with inherited variation affecting Arg441, a site at which variation has been previously seen in NDD-affected siblings, and two individuals have de novo variation resulting in p.Arg1294Cys (c.3880C>T). All variants affect evolutionarily conserved sites, and most are predicted to damage protein structure or function. ZMYM3 is relatively intolerant to variation in the general population, is widely expressed across human tissues, and encodes a component of the KDM1A-RCOR1 chromatin-modifying complex. ChIP-seq experiments on one variant, p.Arg1274Trp, indicate dramatically reduced genomic occupancy, supporting a hypomorphic effect. While we are unable to perform statistical evaluations to definitively support a causative role for variation in ZMYM3, the totality of the evidence, including 27 affected individuals, recurrent variation at two codons, overlapping phenotypic features, protein-modeling data, evolutionary constraint, and experimentally confirmed functional effects strongly support ZMYM3 as an NDD-associated gene.

Ionization and lipophilicity in nonpolar media mimicking the cell membrane interior.

Ionization and lipophilicity may vary with the environment. Therefore, in this study we provide some insight in the performances of different experimental techniques (potentiometry, UV-vis, shake-flask and chromatography) to determine ionization and lipophilicity in more nonpolar systems than those commonly used in drug discovery. To this purpose a pool of 11 compounds of pharmaceutical interest was firstly submitted to a few experimental techniques to measure pKa in water, water/acetonitrile mixtures and pure acetonitrile. Then we measured logP/logD with shake-flask and potentiometry in octanol/water and toluene/water and also determined a chromatographic lipophilicity index (log k'80 PLRP-S) in a nonpolar system. Results show that ionization decreases for both acids and bases in a coherent, significant but not dramatical extent when water is present in the system, but the picture is completely different in pure acetonitrile. Lipophilicity may vary or not with the environment according to the chemical structure of the investigated compounds as also revealed by electrostatic potential maps. Since the internal core of cell membranes is largely nonpolar, our results support the need of extending the pool of physicochemical descriptors to be determined in the various stages of drug discovery programs and indicate some experimental strategies for their determination.

In Silico Tools to Extract the Drug Design Information Content of Degradation Data: The Case of PROTACs Targeting the Androgen Receptor.

Proteolysis-Targeting Chimeras (PROTACs) have recently emerged as a promising technology in the drug discovery landscape. Large interest in the degradation of the androgen receptor (AR) as a new anti-prostatic cancer strategy has resulted in several papers focusing on PROTACs against AR. This study explores the potential of a few in silico tools to extract drug design information from AR degradation data in the format often reported in the literature. After setting up a dataset of 92 PROTACs with consistent AR degradation values, we employed the Bemis-Murcko method for their classification. The resulting clusters were not informative in terms of structure-degradation relationship. Subsequently, we performed Degradation Cliff analysis and identified some key aspects conferring a positive contribution to activity, as well as some methodological limits when applying this approach to PROTACs. Linker structure degradation relationships were also investigated. Then, we built and characterized ternary complexes to validate previous results. Finally, we implemented machine learning classification models and showed that AR degradation for VHL-based but not CRBN-based PROTACs can be predicted from simple permeability-related 2D molecular descriptors.

Personalized Treatment for Infantile Ascending Hereditary Spastic Paralysis Based on In Silico Strategies.

Infantile onset hereditary spastic paralysis (IAHSP) is a rare neurological disease diagnosed in less than 50 children worldwide. It is transmitted with a recessive pattern and originates from mutations of the ALS2 gene, encoding for the protein alsin and involved in differentiation and maintenance of the upper motoneuron. The exact pathogenic mechanisms of IAHSP and other neurodevelopmental diseases are still largely unknown. However, previous studies revealed that, in the cytosolic compartment, alsin is present as an active tetramer, first assembled from dimer pairs. The C-terminal VPS9 domain is a key interaction site for alsin dimerization. Here, we present an innovative drug discovery strategy, which identified a drug candidate to potentially treat a patient harboring two ALS2 mutations: one truncation at lysine 1457 (not considered) and the substitution of arginine 1611 with a tryptophan (R1611W) in the C-terminus VPS9. With a protein modeling approach, we obtained a R1611W mutant model and characterized the impact of the mutation on the stability and flexibility of VPS9. Furthermore, we showed how arginine 1611 is essential for alsin's homo-dimerization and how, when mutated to tryptophan, it leads to an abnormal dimerization pattern, disrupting the formation of active tetramers. Finally, we performed a virtual screening, individuating an already therapy-approved compound (MK4) able to mask the mutant residue and re-establishing the alsin tetramers in HeLa cells. MK4 has now been approved for compassionate use.

Managing Experimental 3D Structures in the Beyond-Rule-of-5 Chemical Space: The Case of Rifampicin.

The beyond-Rule-of-5 (bRo5) chemical space is a source of new oral drugs and includes large and flexible compounds. Because of their size and conformational variability, bRo5 molecules assume different privileged conformations in the compartments of human body, i. e., they can exhibit chameleonic properties. The elucidation of the ensemble of 3D structures explored by such molecules under different conditions is therefore critical to check the role played by chameleonicity to modulate cell permeability. Here we characterized the conformational ensembles of rifampicin, a bRo5 drug, in polar and nonpolar solvents and in the solid state. We performed NMR experiments, analyzed their results with a novel algorithm and set-up a pool of ad hoc in silico strategies to investigate crystallographic structures retrieved from the CSD. Moreover, a polarity descriptor often related to permeability (SA-3D-PSA) was calculated for all the conformers and its variation with the environment analyzed. Results showed that the conformational behavior of rifampicin in solution and in the solid state is not superposable. The identification of dynamic intramolecular hydrogen bonds can be assessed by NMR spectroscopy but not by X-ray structures. Moreover, SA-3D-PSA revealed that dynamic IMHBs do not provide rifampicin with chameleonic properties. Overall, this study highlights that the peculiarity of rifampicin, which is cell permeable probably because of the presence of static IMHBs but is devoid of any chameleonic behavior, can be assessed by a proper analysis of experimental 3D structures.

PROTACs and Building Blocks: The 2D Chemical Space in Very Early Drug Discovery.

Targeted protein degradation by PROTACs has emerged as a new modality for the knockdown of a range of proteins, and, more recently, it has become increasingly clear that the PROTAC chemical space requires characterization through a pool of ad hoc physicochemical descriptors. In this study, a new database named PROTAC-DB that provides extensive information about PROTACs and building blocks was used to obtain the 2D chemical structures of about 1600 PROTACs, 60 E3 ligands, 800 linkers, and 202 warheads. For every structure, we calculated a pool of seven 2D descriptors carefully identified as informative for large and flexible structures. For comparison purposes, the same procedure was applied to a dataset of about 50 bRo5 approved drugs reported in the literature. Correlation matrices, PCAs, box plots, and other graphical tools were used to define and understand the chemical space covered by PROTACs and building blocks in relation to other compounds. Results show that linkers have different properties than E3 ligands and warheads. Polar descriptors additivity is not respected when passing from building blocks to degraders. Moreover, a very preliminary analysis based on three PROTACs with high, intermediate, and low permeability showed how the most permeable compounds seem to occupy a region closer to bRo5 drugs and, thus, exhibit different properties than impermeable compounds. Finally, a second database, PROTACpedia, was used to discuss the relevance of physicochemical descriptors on degradation activity.

Intramolecular hydrogen bonding: An opportunity for improved design in medicinal chemistry.

Recent literature shows that intramolecular hydrogen bond (IMHB) formation can positively impact upon the triad of permeability, solubility, and potency of drugs and candidates. IMHB modulation can be applied to compounds in any chemical space as a means for discovering drug candidates with both acceptable potency and absorption, distribution, metabolism, and excretion-Tox profiles. Integrating IMHB formation in design of drugs is, therefore, an exciting and timely challenge for modern medicinal chemistry. In this review, we first provide some background about IMHBs from the medicinal chemist's point of view and highlight some IMHB-associated misconceptions. Second, we propose a classification of IMHBs for drug discovery purposes, review the most common in silico tactics to include IMHBs in lead optimization and list some experimental physicochemical descriptors, which quantify the propensity of compounds to form IMHBs. By focusing on the compounds size and the number of IMHBs that can potentially be formed, we also outline the major difficulties encountered when designing compounds based on the inclusion of IMHBs. Finally, we discuss recent case studies illustrating the application of IMHB to optimize cell permeability and physicochemical properties of small molecules, cyclic peptides and macrocycles.

Log P as a tool in intramolecular hydrogen bond considerations.

Intramolecular hydrogen bonding (IMHB) considerations are gaining relevance in drug discovery and a molecular descriptor which can predict very early the capacity of a compound to form IMHB is needed to speed up the optimization process of drug candidates. Although log Poct is largely used for optimization purposes, in this paper we firstly use the Block Relevance (BR) analysis to theoretically show how log Poct is not a convenient choice to assess IMHB properties of candidates. Then we discuss the limits of log Poct and introduce Δlog Poct-tol, i.e. the difference between log Poct and log Ptol (the logarithm of the partition coefficient in the toluene/water system). Finally, we provided some examples also including bRo5 protease inhibitors, to clarify how to interpret Δlog Poct-tol values.

Relationship between Passive Permeability and Molecular Polarity Using Block Relevance Analysis.

EPSA is an experimental descriptor of molecular polarity obtained from chromatographic retention in supercritical fluid chromatography (SFC) systems, previously shown by Goetz et al. to correlate with passive permeability of cyclic peptides. The present study focuses on EPSA in relation to passive permeability of small molecules. We applied block relevance (BR) analysis to interpret the relative significance of mechanistic forces prevailing in EPSA. The BR analysis is a computational tool that allows the interpretation of the balance of intermolecular interactions governing systems such as the aforementioned chromatographic retention in EPSA. EPSA and passive permeability determined by Ralph Russ canine kidney cells (RRCK) or low efflux Madin Darby canine kidney cells (MDCK-LE) and human epithelial colorectal adenocarcinoma cells (Caco-2), studied on a data set of commercial drugs, indicated that EPSA is relevant in describing permeability of hydrophilic drugs (CLogP < 1). We then verified, on a data set of 1699 Rule of 5 compliant Pfizer compounds, that when CLogP < 1, a value of EPSA < 100 significantly increases the likelihood of high permeability.

A Fast Chromatographic Method for Estimating Lipophilicity and Ionization in Nonpolar Membrane-Like Environment.

This study describes the design and implementation of a new chromatographic descriptor called log k'80 PLRP-S that provides information about the lipophilicity of drug molecules in the nonpolar environment, both in their neutral and ionized form. The log k'80 PLRP-S obtained on a polymeric column with acetonitrile/water mobile phase is shown to closely relate to log Ptoluene (toluene dielectric constant ε ∼ 2). The main intermolecular interactions governing log k'80 PLRP-S were deconvoluted using the Block Relevance (BR) analysis. The information provided by this descriptor was compared to ElogD and calclog Ptol, and the differences are highlighted. The "charge-flush" concept is introduced to describe the sensitivity of log k'80 PLRP-S to the ionization state of compounds in the pH range 2 to 12. The ability of log k'80 PLRP-S to indicate the propensity of neutral molecules and monoanions to form Intramolecular Hydrogen Bonds (IMHBs) is proven through a number of examples.

Multivariate analysis applied to Raman mapping of dye-functionalized carbon nanotubes: a novel approach to support the rational design of functional nanostructures.

Principal component analysis is applied to analyse the Raman maps collected on carbon nanotubes at different degrees of oxidation and functionalization with dye labeling molecules. The results are used to demonstrate that the technique is extremely effective in clustering data and comparing preparation protocols, so that it enables drawing of a fast and reliable classification of the molecule propensity to interact with pristine and oxidized carbon nanotubes. The spectral findings are supported and elucidated by several experimental techniques, thermogravimetry and steady-state and time-resolved fluorescence measurements, and by computational modeling, showing that the proposed methodology could represent a powerful and routine test for the rational design of functional nanostructures.

Prediction and interpretation of the lipophilicity of small peptides.

Peptide-based drug discovery has considerably expanded and solid in silico tools for the prediction of physico-chemical properties of peptides are urgently needed. In this work we tested some combinations of descriptors/algorithms to find the best model to predict [Formula: see text] of a series of peptides. To do that we evaluate the models statistical performances but also their skills in providing a reliable deconvolution of the balance of intermolecular forces governing the partitioning phenomenon. Results prove that a PLS model based on VolSurf+ descriptors is the best tool to predict [Formula: see text] of neutral and ionised peptides. The mechanistic interpretation also reveals that the inclusion in the chemical structure of a HBD group is more efficient in decreasing lipophilicity than the inclusion of a HBA group.

IMHB-Mediated Chameleonicity in Drug Design: A Focus on Structurally Related PROTACs.

Molecular chameleonicity may enable compounds to compensate for the unfavorable ADME properties typically associated with complex molecules, such as PROTACs. Here we present a few in silico strategies to implement chameleonicity considerations in drug design. Initially, we identified six structurally related CRBN-based PROTACs targeting BET proteins and experimentally verified whether chameleonicity is needed to obtain an acceptable physicochemical profile. Then, we utilized experimental data to validate our novel computational strategies based on tools crafted to encompass a spectrum of complexities and innovative features. After confirming that the formation of IMHBs is the primary driving factor behind chameleonicity, we initially utilized conformational sampling data to define cChameCS, an IMHB-mediated, simple, and rapid chameleonicity predictor index suitable for early drug discovery. Subsequently, we identified dynamic IMHB patterns relevant to chameleonicity through molecular dynamics simulations. Finally, we proposed a workflow for designing structurally related chameleonic PROTACs of potential application in the lead optimization process.

DegraderTCM: A Computationally Sparing Approach for Predicting Ternary Degradation Complexes.

Proteolysis targeting chimeras (PROTACs or degraders) represent a novel therapeutic modality that has raised interest thanks to promising results and currently undergoing clinical testing. PROTACs induce the selective proteasomal degradation of undesired proteins by the formation of ternary complexes (TCs). Having knowledge of the 3D structure of TCs is crucial for the design of PROTAC drugs. Here, we describe DegraderTCM, a new computational method for modeling PROTAC-mediated TCs that requires low computational power and provides sound results in a short time span. We validated DegraderTCM against a selected set of experimentally determined structures and defined a method to predict the PROTAC degradation activity based on the computed TC structure. Finally, we modeled TCs of known degraders holding significance for defining the method's applicability domain. A retrospective analysis of structure-activity relationships unveiled possibilities for utilizing DegraderTCM in the initial stages of designing novel PROTAC drugs.

Exploring the chemical space of orally bioavailable PROTACs.

A principal challenge in the discovery of proteolysis targeting chimeras (PROTACs) as oral medications is their bioavailability. To facilitate drug design, it is therefore essential to identify the chemical space where orally bioavailable PROTACs are more likely to be situated. To this aim, we extracted structure-bioavailability insights from published data using traditional 2D descriptors, thereby shedding light on their potential and limitations as drug design tools. Subsequently, we describe cutting-edge experimental, computational and hybrid design strategies based on 3D descriptors, which show promise for enhancing the probability of discovering PROTACs with high oral bioavailability.

The Quest for Oral PROTAC drugs: Evaluating the Weaknesses of the Screening Pipeline.

A targeted bibliographic search exposed the deficiencies within existing PROTAC preclinical pipelines, including missing, poor-quality data and technical limitations in the experimental assays. Several recommendations are proposed to improve the efficiency of preclinical platforms for PROTACs.

Conformational Sampling Deciphers the Chameleonic Properties of a VHL-Based Degrader.

Chameleonicity (the capacity of a molecule to adapt its conformations to the environment) may help to identify orally bioavailable drugs in the beyond-Rule-of-5 chemical space. Computational methods to predict the chameleonic behaviour of degraders have not yet been reported and the identification of molecular chameleons still relies on experimental evidence. Therefore, there is a need to tune predictions with experimental data. Here, we employ PROTAC-1 (a passively cell-permeable degrader), for which NMR and physicochemical data prove the chameleonic behaviour, to benchmark the capacity of two conformational sampling algorithms and selection schemes. To characterize the conformational ensembles in both polar and nonpolar environments, we compute three molecular properties proven to be essential for cell permeability: conformer shape (radius of gyration), polarity (3D PSA), and the number of intramolecular hydrogen bonds. Energetic criteria were also considered. Infographics monitored the simultaneous variation of those properties in computed and NMR conformers. Overall, we provide key points for tuning conformational sampling tools to reproduce PROTAC-1 chameleonicity according to NMR evidence. This study is expected to improve the design of PROTAC drugs and the development of computational sustainable strategies to exploit the potential of new modalities in drug discovery.

High-throughput and reliable assessments of the ionization constant of monoprotic organic acids through an arginine based mixed mode HPLC.

The charge state of a molecule is the single most prominent attribute ruling out its interactions with the surrounding environment. In a previous study, the retention of acids on the new Celeris™ Arginine (ARG) column was found to be predominantly driven by electrostatics and, specifically, their charge state. Therefore, we analysed 41 compounds in liquid chromatography with ultraviolet detection to study possible relationships between the analytical retention on this phase and the pKa of the acidic solutes. Highly significant relationships were observed indicating either a linear (r2 = 0.86) or a quadratic (r2= 0.89) trend. To improve the throughput of the method, this was transferred to LC mass spectrometry, allowing the analysis of a molecule every 3 mins. The developed method was found to be fast, reliable, accurate, easily automatable and simple to set up. Finally, the analytical column's being industrially manufactured and commercially available offers broad applicability.

Chamelogk: A Chromatographic Chameleonicity Quantifier to Design Orally Bioavailable Beyond-Rule-of-5 Drugs.

New chemical modalities in drug discovery include molecules belonging to the bRo5 chemical space. Because of their complex and flexible structure, bRo5 compounds often suffer from a poor solubility/permeability profile. Chameleonicity describes the capacity of a molecule to adapt to the environment through conformational changes; the design of molecular chameleons is a medicinal chemistry strategy simultaneously optimizing solubility and permeability. A default method to quantify chameleonicity in early drug discovery is still missing. Here we introduce Chamelogk, an automated, fast, and cheap chromatographic descriptor of chameleonicity. Moreover, we report measurements for 55 Ro5 and bRo5 compounds and validate our method with literature data. Then, selected case studies (macrocycles, nonmacrocyclic compounds, and PROTACs) are used to illustrate the application of Chamelogk in combination with lipophilicity (BRlogD) and polarity (Δ log kwIAM) descriptors. Overall, we show how Chamelogk deserves being included in property-based drug discovery strategies to design oral bioavailable bRo5 compounds.

AI-based protein structure databases have the potential to accelerate rare diseases research: AlphaFoldDB and the case of IAHSP/Alsin.

Artificial intelligence (AI)-based protein structure databases are expected to have an impact on drug discovery. Here, we show how AlphaFold could support rare diseases research programs. We focus on Alsin, a protein responsible for rare motor neuron diseases, such as infantile-onset ascending hereditary spastic paralysis (IAHSP) and juvenile primary lateral sclerosis (JPLS), and involved in some cases of amyotrophic lateral sclerosis (ALS). First, we compared the AlphaFoldDB human Alsin model with homology models of Alsin domains. We then evaluated the flexibility profile of Alsin and of experimentally characterized mutants present in patients with IAHSP. Next, we compared preliminary models of dimeric/tetrameric Alsin responsible for its physiological action with hypothetical models reported in the literature. Finally, we suggest the best animal model for drug candidates testing. Overall, we computationally show that drug discovery efforts toward Alsin-involving diseases should be pursued.