Internal Structure and Preferential Protein Binding of Colloidal Aggregates

Predictions of Colloidal Molecular Aggregation Using AI/ML Models

To facilitate the triage of hits from small molecule screens, we have used various AI/ML techniques and experimentally observed data sets to build models aimed at predicting colloidal aggregation of small organic molecules in aqueous solution. We have found that Naïve Bayesian and deep neural networks outperform logistic regression, recursive partitioning tree, support vector machine, and random forest techniques by having the lowest balanced error rate (BER) for the test set. Derived predictive classification models consistently and successfully discriminated aggregator molecules from nonaggregator hits. An analysis of molecular descriptors in favor of colloidal aggregation confirms previous observations (hydrophobicity, molecular weight, and solubility) in addition to undescribed molecular descriptors such as the fraction of sp3 carbon atoms (Fsp3), and electrotopological state of hydroxyl groups (ES_Sum_sOH). Naïve Bayesian modeling and scaffold tree analysis have revealed chemical features/scaffolds contributing the most to colloidal aggregation and nonaggregation, respectively. These results highlight the importance of scaffolds with high Fsp3 values in promoting nonaggregation. Matched molecular pair analysis (MMPA) has also deciphered context-dependent substitutions, which can be used to design nonaggregator molecules. We found that most matched molecular pairs have a neutral effect on aggregation propensity. We have prospectively applied our predictive models to assist in chemical library triage for optimal plate selection diversity and purchase for high throughput screening (HTS) in drug discovery projects.

Supramolecular dye nanoassemblies for advanced diagnostics and therapies

Dyes have conventionally been used in medicine for staining cells, tissues, and organelles. Since these compounds are also known as photosensitizers (PSs) which exhibit photoresponsivity upon photon illumination, there is a high desire towards formulating these molecules into nanoparticles (NPs) to achieve improved delivery efficiency and enhanced stability for novel imaging and therapeutic applications. Furthermore, it has been shown that some of the photophysical properties of these molecules can be altered upon NP formation thereby playing a major role in the outcome of their application. In this review, we primarily focus on introducing dye categories, their formulation strategies and how these strategies affect their photophysical properties in the context of photothermal and non-photothermal applications. More specifically, the most recent progress showing the potential of dye supramolecular assemblies in modalities such as photoacoustic and fluorescence imaging, photothermal and photodynamic therapies as well as their employment in photoablation as a novel modality will be outlined. Aside from their photophysical activity, we delve shortly into the emerging application of dyes as drug stabilizing agents where these molecules are used together with aggregator molecules to form stable nanoparticles.

Molecular dynamics simulations as a guide for modulating small molecule aggregation

Small colloidally aggregating molecules (SCAMs) can be problematic for biological assays in drug discovery campaigns. However, the self-associating properties of SCAMs have potential applications in drug delivery and analytical biochemistry. Consequently, the ability to predict the aggregation propensity of a small organic molecule is of considerable interest. Chemoinformatics-based filters such as ChemAGG and Aggregator Advisor offer rapid assessment but are limited by the assay quality and structural diversity of their training set data. Complementary to these tools, we explore here the ability of molecular dynamics (MD) simulations as a physics-based method capable of predicting the aggregation propensity of diverse chemical structures. For a set of 32 molecules, using simulations of 100 ns in explicit solvent, we find a success rate of 97% (one molecule misclassified) as opposed to 75% by Aggregator Advisor and 72% by ChemAGG. These short timescale MD simulations are representative of longer microsecond trajectories and yield an informative spectrum of aggregation propensities across the set of solutes, capturing the dynamic behaviour of weakly aggregating compounds. Implicit solvent simulations using the generalized Born model were less successful in predicting aggregation propensity. MD simulations were also performed to explore structure-aggregation relationships for selected molecules, identifying chemical modifications that reversed the predicted behaviour of a given aggregator/non-aggregator compound. While lower throughput than rapid cheminformatics-based SCAM filters, MD-based prediction of aggregation has potential to be deployed on the scale of focused subsets of moderate size, and, depending on the target application, provide guidance on removing or optimizing a compound's aggregation propensity.

The Potential of Congo Red Supplied Aggregates of Multitargeted Tyrosine Kinase Inhibitor (Sorafenib, BAY-43-9006) in Enhancing Therapeutic Impact on Bladder Cancer

Bladder cancer is a common malignancy associated with high recurrence rates and potential progression to invasive forms. Sorafenib, a multi-targeted tyrosine kinase inhibitor, has shown promise in anti-cancer therapy, but its cytotoxicity to normal cells and aggregation in solution limits its clinical application. To address these challenges, we investigated the formation of supramolecular aggregates of sorafenib with Congo red (CR), a bis-azo dye known for its supramolecular interaction. We analyzed different mole ratios of CR-sorafenib aggregates and evaluated their effects on bladder cancer cells of varying levels of malignancy. In addition, we also evaluated the effect of the test compounds on normal uroepithelial cells. Our results demonstrated that sorafenib inhibits the proliferation of bladder cancer cells and induces apoptosis in a dose-dependent manner. However, high concentrations of sorafenib also showed cytotoxicity to normal uroepithelial cells. In contrast, the CR-BAY aggregates exhibited reduced cytotoxicity to normal cells while maintaining anti-cancer activity. The aggregates inhibited cancer cell migration and invasion, suggesting their potential for metastasis prevention. Dynamic light scattering and UV-VIS measurements confirmed the formation of stable co-aggregates with distinctive spectral properties. These CR-sorafenib aggregates may provide a promising approach to targeted therapy with reduced cytotoxicity and improved stability for drug delivery in bladder cancer treatment. This work shows that the drug-excipient aggregates proposed and described so far, as Congo red-sorafenib, can be a real step forward in anti-cancer therapies.

Fragment-based drug nanoaggregation reveals drivers of self-assembly

Drug nanoaggregates are particles that can deleteriously cause false positive results during drug screening efforts, but alternatively, they may be used to improve pharmacokinetics when developed for drug delivery purposes. The structural features of molecules that drive nanoaggregate formation remain elusive, however, and the prediction of intracellular aggregation and rational design of nanoaggregate-based carriers are still challenging. We investigate nanoaggregate self-assembly mechanisms using small molecule fragments to identify the critical molecular forces that contribute to self-assembly. We find that aromatic groups and hydrogen bond acceptors/donors are essential for nanoaggregate formation, suggesting that both π-π stacking and hydrogen bonding are drivers of nanoaggregation. We apply structure-assembly-relationship analysis to the drug sorafenib and discover that nanoaggregate formation can be predicted entirely using drug fragment substructures. We also find that drug nanoaggregates are stabilized in an amorphous core-shell structure. These findings demonstrate that rational design can address intracellular aggregation and pharmacologic/delivery challenges in conventional and fragment-based drug development processes.

Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds

Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with assay detection technology. In response, we generated the largest publicly available library of chemical liabilities and developed "Liability Predictor," a free web tool to predict HTS artifacts. More specifically, we generated, curated, and integrated HTS data sets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity and developed and validated quantitative structure-interference relationship (QSIR) models to predict these nuisance behaviors. The resulting models showed 58-78% external balanced accuracy for 256 external compounds per assay. QSIR models developed and validated herein identify nuisance compounds among experimental hits more reliably than do popular PAINS filters. Both the models and the curated data sets were implemented in "Liability Predictor," publicly available at https://liability.mml.unc.edu/. "Liability Predictor" may be used as part of chemical library design or for triaging HTS hits.

Discovery of Kinase and Carbonic Anhydrase Dual Inhibitors by Machine Learning Classification and Experiments

A multi-target small molecule modulator is advantageous for treating complicated diseases such as cancers. However, the strategy and application for discovering a multi-target modulator have been less reported. This study presents the dual inhibitors for kinase and carbonic anhydrase (CA) predicted by machine learning (ML) classifiers, and validated by biochemical and biophysical experiments. ML trained by CA I and CA II inhibitor molecular fingerprints predicted candidates from the protein-specific bioactive molecules approved or under clinical trials. For experimental tests, three sulfonamide-containing kinase inhibitors, 5932, 5946, and 6046, were chosen. The enzyme assays with CA I, CA II, CA IX, and CA XII have allowed the quantitative comparison in the molecules’ inhibitory activities. While 6046 inhibited weakly, 5932 and 5946 exhibited potent inhibitions with 100 nM to 1 μM inhibitory constants. The ML screening was extended for finding CAs inhibitors of all known kinase inhibitors. It found XMU-MP-1 as another potent CA inhibitor with an approximate 30 nM inhibitory constant for CA I, CA II, and CA IX. Differential scanning fluorimetry confirmed the direct interaction between CAs and small molecules. Cheminformatics studies, including docking simulation, suggest that each molecule possesses two separate functional moieties: one for interaction with kinases and the other with CAs.

Hydroxamic Acid as a Potent Metal-Binding Group for Inhibiting Tyrosinase

Tyrosinase, a metalloenzyme containing a dicopper cofactor, plays a central role in synthesizing melanin from tyrosine. Many studies have aimed to identify small-molecule inhibitors of tyrosinase for pharmaceutical, cosmetic, and agricultural purposes. In this study, we report that hydroxamic acid is a potent metal-binding group for interacting with dicopper atoms, thereby inhibiting tyrosinase. Hydroxamate-containing molecules, including anticancer drugs targeting histone deacetylase, vorinostat and panobinostat, significantly inhibited mushroom tyrosinase, with inhibitory constants in the submicromolar range. Of the tested molecules, benzohydroxamic acid was the most potent. Its inhibitory constant of 7 nM indicates that benzohydroxamic acid is one of the most potent tyrosinase inhibitors. Results from differential scanning fluorimetry revealed that direct binding mediates inhibition. The enzyme kinetics were studied to assess the inhibitory mechanism of the hydroxamate-containing molecules. Experiments with B16F10 cell lysates confirmed that the new inhibitors are inhibitory against mammalian tyrosinase. Docking simulation data revealed intermolecular contacts between hydroxamate-containing molecules and tyrosinase.

Probing the free-state solution behavior of drugs and their tendencies to self-aggregate into nano-entities

The free-state solution behaviors of drugs profoundly affect their properties. Therefore, it is critical to properly evaluate a drug's unique multiphase equilibrium when in an aqueous enviroment, which can comprise lone molecules, self-associating aggregate states and solid phases. To date, the full range of nano-entities that drugs can adopt has been a largely unexplored phenomenon. This protocol describes how to monitor the solution behavior of drugs, revealing the nano-entities formed as a result of self-associations. The procedure begins with a simple NMR 1H assay, and depending on the observations, subsequent NMR dilution, NMR T2-CPMG (spin-spin relaxation Carr-Purcell-Meiboom-Gill) and NMR detergent assays are used to distinguish between the existence of fast-tumbling lone drug molecules, small drug aggregates and slow-tumbling colloids. Three orthogonal techniques (dynamic light scattering, transmission electron microscopy and confocal laser scanning microscopy) are also described that can be used to further characterize any large colloids. The protocol can take a non-specialist between minutes to a few hours; thus, libraries of compounds can be evaluated within days.

Gains from no real PAINS: Where 'Fair Trial Strategy' stands in the development of multi-target ligands

Compounds that selectively modulate multiple targets can provide clinical benefits and are an alternative to traditional highly selective agents for unique targets. High-throughput screening (HTS) for multitarget-directed ligands (MTDLs) using approved drugs, and fragment-based drug design has become a regular strategy to achieve an ideal multitarget combination. However, the unexpected presence of pan-assay interference compounds (PAINS) suspects in the development of MTDLs frequently results in nonspecific interactions or other undesirable effects leading to artefacts or false-positive data of biological assays. Publicly available filters can help to identify PAINS suspects; however, these filters cannot comprehensively conclude whether these suspects are "bad" or innocent. Additionally, these in silico approaches may inappropriately label a ligand as PAINS. More than 80% of the initial hits can be identified as PAINS by the filters if appropriate biochemical tests are not used resulting in false positive data that are unacceptable for medicinal chemists in manuscript peer review and future studies. Therefore, extensive offline experiments should be used after online filtering to discriminate "bad" PAINS and avoid incorrect evaluation of good scaffolds. We suggest that the use of "Fair Trial Strategy" to identify interesting molecules in PAINS suspects to provide certain structure‒function insight in MTDL development.

Preparing (Metalla)carboranes for Nanomedicine

"There's plenty of room at the bottom" (Richard Feynman, 1959): an invitation for (metalla)carboranes to enter the (new) field of nanomedicine. For two decades, the number of publications on boron cluster compounds designed for potential applications in medicine has been constantly increasing. Hundreds of compounds have been screened in vitro or in vivo for a variety of biological activities (chemotherapeutics, radiotherapeutics, antiviral, etc.), and some have shown rather promising potential for further development. However, until now, no boron cluster compounds have made it to the clinic, and even clinical trials have been very sparse. This review introduces a new perspective in the field of medicinal boron chemistry, namely that boron-based drugs should be regarded as nanomedicine platforms, due to their peculiar self-assembly behaviour in aqueous solutions, and treated as such. Examples for boron-based 12- and 11-vertex clusters and appropriate comparative studies from medicinal (in)organic chemistry and nanomedicine, highlighting similarities, differences and gaps in physicochemical and biological characterisation methods, are provided to encourage medicinal boron chemists to fill in the gaps between chemistry laboratory and real applications in living systems by employing bioanalytical and biophysical methods for characterising and controlling the aggregation behaviour of the clusters in solution.

A Crowding Barrier to Protein Inhibition in Colloidal Aggregates

Small molecule colloidal aggregates adsorb and partially denature proteins, inhibiting them artifactually. Oddly, this inhibition is typically time-dependent. Two mechanisms might explain this: low concentrations of the colloid and enzyme might mean low encounter rates, or colloid-based protein denaturation might impose a kinetic barrier. These two mechanisms should have different concentration dependencies. Perplexingly, when enzyme concentration was increased, incubation times actually lengthened, inconsistent with both models and with classical chemical kinetics of solution species. We therefore considered molecular crowding, where colloids with lower protein surface density demand a shorter incubation time than more crowded colloids. To test this, we grew and shrank colloid surface area. As the surface area shrank, the incubation time lengthened, while as it increased, the converse was true. These observations support a crowding effect on protein binding to colloidal aggregates. Implications for drug delivery and for detecting aggregation-based inhibition will be discussed.

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

SCAM Detective: Accurate Predictor of Small, Colloidally Aggregating Molecules

Small, colloidally aggregating molecules (SCAMs) are the most common source of false positives in high-throughput screening (HTS) campaigns. Although SCAMs can be experimentally detected and suppressed by the addition of detergent in the assay buffer, detergent sensitivity is not routinely monitored in HTS. Computational methods are thus needed to flag potential SCAMs during HTS triage. In this study, we have developed and rigorously validated quantitative structure-interference relationship (QSIR) models of detergent-sensitive aggregation in several HTS campaigns under various assay conditions and screening concentrations. In particular, we have modeled detergent-sensitive aggregation in an AmpC β-lactamase assay, the preferred HTS counter-screen for aggregation, as well as in another assay that measures cruzain inhibition. Our models increase the accuracy of aggregation prediction by ∼53% in the β-lactamase assay and by ∼46% in the cruzain assay compared to previously published methods. We also discuss the importance of both assay conditions and screening concentrations in the development of QSIR models for various interference mechanisms besides aggregation. The models developed in this study are publicly available for fast prediction within the SCAM detective web application (https://scamdetective.mml.unc.edu/).

How Do Small Molecule Aggregates Inhibit Enzyme Activity? A Molecular Dynamics Study

Small molecule compounds which form colloidal aggregates in solution are problematic in early drug discovery; adsorption of the target protein by these aggregates can lead to false positives in inhibition assays. In this work, we probe the molecular basis of this inhibitory mechanism using molecular dynamics simulations. Specifically, we examine in aqueous solution the adsorption of the enzymes β-lactamase and PTP1B onto aggregates of the drug miconazole. In accordance with experiment, molecular dynamics simulations observe formation of miconazole aggregates as well as subsequent association of these aggregates with β-lactamase and PTP1B. When complexed with aggregate, the proteins do not exhibit significant alteration in protein tertiary structure or dynamics on the microsecond time scale of the simulations, but they do indicate persistent occlusion of the protein active site by miconazole molecules. MD simulations further suggest this occlusion can occur via surficial interactions of protein with miconazole but also potentially by envelopment of the protein by miconazole. The heterogeneous polarity of the miconazole aggregate surface seems to underpin its activity as an invasive and nonspecific inhibitory agent. A deeper understanding of these protein/aggregate systems has implications not only for drug design but also for their exploitation as tools in drug delivery and analytical biochemistry.

Protein Stability Effects in Aggregate-Based Enzyme Inhibition

Small-molecule aggregates are a leading cause of artifacts in early drug discovery, but little is known about their interactions with proteins, nor why some proteins are more susceptible to inhibition than others. A possible reason for this apparent selectivity is that aggregation-based inhibition, as a stoichiometric process, is sensitive to protein concentration, which varies across assays. Alternatively, local protein unfolding by aggregates may lead to selectivity since stability varies among proteins. To deconvolute these effects, we used differentially stable point mutants of a single protein, TEM-1 β-lactamase. Broadly, destabilized mutants had higher affinities for and were more potently inhibited by aggregates versus more stable variants. The addition of the irreversible inhibitor moxalactam destabilized several mutants, and these typically bound tighter to a colloidal particle, while the only mutant it stabilized bound weaker. These results suggest that less-stable enzymes are more easily sequestered and inhibited by colloidal aggregates.

Exposing Small-Molecule Nanoentities by a Nuclear Magnetic Resonance Relaxation Assay

Small molecules can self-assemble in aqueous solution into a wide range of nanoentity types and sizes (dimers, n-mers, micelles, colloids, etc.), each having their own unique properties. This has important consequences in the context of drug discovery including issues related to nonspecific binding, off-target effects, and false positives and negatives. Here, we demonstrate the use of the spin-spin relaxation Carr-Purcell-Meiboom-Gill NMR experiment, which is sensitive to molecular tumbling rates and can expose larger aggregate species that have slower rotational correlations. The strategy easily distinguishes lone-tumbling molecules versus nanoentities of various sizes. The technique is highly sensitive to chemical exchange between single-molecule and aggregate states and can therefore be used as a reporter when direct measurement of aggregates is not possible by NMR. Interestingly, we found differences in solution behavior for compounds within structurally related series, demonstrating structure-nanoentity relationships. This practical experiment is a valuable tool to support drug discovery efforts.

Computational advances in combating colloidal aggregation in drug discovery

Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.

Effect of tolytoxin on tunneling nanotube formation and function

Tunneling nanotubes (TNTs) are actin-containing membrane protrusions that play an essential role in long-range intercellular communication. They are involved in development of various diseases by allowing transfer of pathogens or protein aggregates as well as organelles such as mitochondria. Increase in TNT formation has been linked to many pathological conditions. Here we show that nM concentrations of tolytoxin, a cyanobacterial macrolide that targets actin by inhibition of its polymerization, significantly decrease the number of TNT-connected cells, as well as transfer of mitochondria and α-synuclein fibrils in two different cell lines of neuronal (SH-SY5Y) and epithelial (SW13) origin. As the cytoskeleton of the tested cell remain preserved, this macrolide could serve as a valuable tool for future therapies against diseases propagated by TNTs.

Binding Assays for Bromodomain Proteins: Their Utility in Drug Discovery in Oncology and Inflammatory Disease

Bromodomains are protein domains that recognize acetylated lysine residues and are important for recruiting a large number of protein and multiprotein complexes to sites of lysine acetylation. They play an important role in chromatin biology and are popular targets for drug discovery. Compound screening in this area requires the use of biochemical assays to assess the binding potency of potential drug candidates. Foremost among the efforts to target bromodomains are those aimed at identifying compounds that interact with the bromodomain and extra-terminal domain (BET) family of bromodomain-containing proteins (BRD2, BRD3, BRD4, and BRDT). Inhibitors of these proteins are under clinical development for a large variety of oncologic indications. Described in this unit are several assays to assess the binding potency and selectivity within the BET protein family. Included are AlphaScreen, fluorescence polarization, and thermal shift assays. The strengths and weaknesses of each assay are discussed. © 2018 by John Wiley & Sons, Inc.

Colloidal aggregation: from screening nuisance to formulation nuance

It is well known that small molecule colloidal aggregation is a leading cause of false positives in early drug discovery. Colloid-formers are diverse and well represented among corporate and academic screening decks, and even among approved drugs. Less appreciated is how colloid formation by drug-like compounds fits into the wider understanding of colloid physical chemistry. Here we introduce the impact that colloidal aggregation has had on early drug discovery, and then turn to the physical and thermodynamic driving forces for small molecule colloidal aggregation, including the particulate nature of the colloids, their critical aggregation concentration-governed formation, their mechanism of protein adsorption and subsequent inhibition, and their sensitivity to detergent. We describe methods that have been used extensively to both identify aggregate-formers and to study and control their physical chemistry. While colloidal aggregation is widely recognized as a problem in early drug discovery, we highlight the opportunities for exploiting this phenomenon in biological milieus and for drug formulation.

Comparative Molecular Dynamics Simulation of Aggregating and Non-Aggregating Inhibitor Solutions: Understanding the Molecular Basis of Promiscuity

The presence of false positives in enzyme inhibition assays is a common problem in early drug discovery, especially for compounds that form colloid aggregates in solution. The molecular basis of these aggregates could not be thoroughly explored because of their transient stability. In this study we conducted comparative molecular dynamics (MD) simulations of miconazole, a strong aggregator, and fluconazole, a known non-aggregator. Interestingly, miconazole displays full aggregation within only 50 ns, whilst fluconazole shows no aggregation over the 500 ns simulation. The simulations indicate that the center of the aggregate is densely packed by the hydrophobic groups of miconazole, whereas polar and nonpolar groups comprise the surface to form a micelle-like colloid. The amphiphilic moment and planar nature of the miconazole structure appear to promote its aggregating behavior. The simulations also predict rapid aggregate formation for a second known promiscuous inhibitor, nicardipine. Thus, MD appears to be a useful tool to characterize aggregate-prone inhibitors at molecular-level detail and has the potential to provide useful information for drug discovery and formulation design.