Merging allosteric and active site binding motifs: de novo generation of target selectivity and potency via natural-product-derived fragments

Discovery of matrix metalloproteinase inhibitors as anti-skin photoaging agents

Photodamage is the result of prolonged exposure of the skin to sunlight. This exposure causes an overexpression of matrix metalloproteinases (MMPs), leading to the abnormal degradation of collagen in the skin tissue and resulting in skin aging and damage. This review presents a detailed overview of MMPs as a potential target for addressing skin aging. Specifically, we elucidated the precise mechanisms by which MMP inhibitors exert their anti-photoaging effects. Furthermore, we comprehensively analyzed the current research progress on MMP inhibitors that demonstrate significant inhibitory activity against MMPs and anti-skin photoaging effects. The review also provides insights into the structure-activity relationships of these inhibitors. Our objective in conducting this review is to provide valuable practical information to researchers engaged in investigations on anti-skin photoaging.

Artificial intelligence for natural product drug discovery

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Discovery of N-Phenylpropiolamide as a Novel Succinate Dehydrogenase Inhibitor Scaffold with Broad-Spectrum Antifungal Activity on Phytopathogenic Fungi

Based on the structural features of both succinate dehydrogenase inhibitors (SDHIs) and targeted covalent inhibitors, a series of N-phenylpropiolamides containing a Michael acceptor moiety were designed to find new antifungal compounds. Nineteen compounds showed potent inhibition activity in vitro on nine species of plant pathogenic fungi. Compounds 9 and 13 showed higher activity on most of the fungi than the standard drug azoxystrobin. Compound 13 could completely inhibit Physalospora piricola infection on apples at 200 μg/mL concentration over 7 days and showed high safety to seed germination and seedling growth of plants at ≤100 μg/mL concentration. The action mechanism showed that 13 is an SDH inhibitor with a median inhibitory concentration, IC₅₀, value of 0.55 μg/mL, comparable with that of the positive drug boscalid. Molecular docking studies revealed that 13 can bind well to the ubiquinone-binding region of SDH by hydrogen bonds and undergoes π-alkyl interaction and π-cation interaction. At the cellular level, 1 as the parent compound could destruct the mycelial structure of P. piricola and partly dissolve the cell wall and/or membrane. Structure-activity relationship analysis showed that the acetenyl group should be a structure determinant for the activity, and the substitution pattern of the phenyl ring can significantly impact the activity. Thus, N-phenylpropiolamide emerged as a novel and promising lead scaffold for the development of new SDHIs for plant protection.

Fragment-to-lead tailored in silico design

Fragment-based drug discovery (FBDD) emerged as a disruptive technology and became established during the last two decades. Its rationality and low entry costs make it appealing, and the numerous examples of approved drugs discovered through FBDD validate the approach. However, FBDD still faces numerous challenges. Perhaps the most important one is the transformation of the initial fragment hits into viable leads. Fragment-to-lead (F2L) optimization is resource-intensive and is therefore limited in the possibilities that can be actively pursued. In silico strategies play an important role in F2L, as they can perform a deeper exploration of chemical space, prioritize molecules with high probabilities of being active and generate non-obvious ideas. Here we provide a critical overview of current in silico strategies in F2L optimization and highlight their remarkable impact. While very effective, most solutions are target- or fragment- specific. We propose that fully integrated in silico strategies, capable of automatically and systematically exploring the fast-growing available chemical space can have a significant impact on accelerating the release of fragment originated drugs.

Non-Extensive Fragmentation of Natural Products and Pharmacophore-Based Virtual Screening as a Practical Approach to Identify Novel Promising Chemical Scaffolds

Fragment-based drug design (FBDD) and pharmacophore modeling have proven to be efficient tools to discover novel drugs. However, these approaches may become limited if the collection of fragments is highly repetitive, poorly diverse, or excessively simple. In this article, combining pharmacophore modeling and a non-classical type of fragmentation (herein called non-extensive) to screen a natural product (NP) library may provide fragments predicted as potent, diverse, and developable. Initially, we applied retrosynthetic combinatorial analysis procedure (RECAP) rules in two versions, extensive and non-extensive, in order to deconstruct a virtual library of NPs formed by the databases Traditional Chinese Medicine (TCM), AfroDb (African Medicinal Plants database), NuBBE (Nuclei of Bioassays, Biosynthesis, and Ecophysiology of Natural Products), and UEFS (Universidade Estadual de Feira de Santana). We then developed a virtual screening (VS) using two groups of natural-product-derived fragments (extensive and non-extensive NPDFs) and two overlapping pharmacophore models for each of 20 different proteins of therapeutic interest. Molecular weight, lipophilicity, and molecular complexity were estimated and compared for both types of NPDFs (and their original NPs) before and after the VS proceedings. As a result, we found that non-extensive NPDFs exhibited a much higher number of chemical entities compared to extensive NPDFs (45,355 vs. 11,525 compounds), accounting for the larger part of the hits recovered and being far less repetitive than extensive NPDFs. The structural diversity of both types of NPDFs and the NPs was shown to diminish slightly after VS procedures. Finally, and most interestingly, the pharmacophore fit score of the non-extensive NPDFs proved to be not only higher, on average, than extensive NPDFs (56% of cases) but also higher than their original NPs (69% of cases) when all of them were also recognized as hits after the VS. The findings obtained in this study indicated that the proposed cascade approach was useful to enhance the probability of identifying innovative chemical scaffolds, which deserve further development to become drug-sized candidate compounds. We consider that the knowledge about the deconstruction degree required to produce NPDFs of interest represents a good starting point for eventual synthesis, characterization, and biological activity studies.

Matrix Metalloproteinase 13 Inhibitors for Modulation of Osteoclastogenesis: Enhancement of Solubility and Stability

Matrix metalloproteinase 13 (MMP-13) activity has been correlated to breast cancer bone metastasis. It has been proposed that MMP-13 contributes to bone metastasis through the promotion of osteoclastogenesis. To explore the mechanisms of MMP-13 action, we previously described a highly efficacious and selective MMP-13 inhibitor, RF036. Unfortunately, further pursuit of RF036 as a probe of MMP-13 in vitro and in vivo activities was not practical due to the limited solubility and stability of the inhibitor. Our new study has explored replacing the RF036 backbone sulfur atom and terminal methyl group to create inhibitors with more favorable pharmacokinetic properties. One compound, designated inhibitor 3, in which the backbone sulfur and terminal methyl group of RF036 were replaced by nitrogen and oxetane, respectively, had comparable activity, selectivity, and membrane permeability to RF036, while exhibiting greatly enhanced solubility and stability. Inhibitor 3 effectively inhibited MMP-13-mediated osteoclastogenesis but spared collagenolysis, and thus represents a next-generation MMP-13 probe applicable for in vivo studies of breast cancer metastasis.

Exosite inhibition of ADAMTS-5 by a glycoconjugated arylsulfonamide

ADAMTS-5 is a major protease involved in the turnover of proteoglycans such as aggrecan and versican. Dysregulated aggrecanase activity of ADAMTS-5 has been directly linked to the etiology of osteoarthritis (OA). For this reason, ADAMTS-5 is a pharmaceutical target for the treatment of OA. ADAMTS-5 shares high structural and functional similarities with ADAMTS-4, which makes the design of selective inhibitors particularly challenging. Here we exploited the ADAMTS-5 binding capacity of β-N-acetyl-d-glucosamine to design a new class of sugar-based arylsulfonamides. Our most promising compound, 4b, is a non-zinc binding ADAMTS-5 inhibitor which showed high selectivity over ADAMTS-4. Docking calculations combined with molecular dynamics simulations demonstrated that 4b is a cross-domain inhibitor that targets the interface of the metalloproteinase and disintegrin-like domains. Furthermore, the interaction between 4b and the ADAMTS-5 Dis domain is mediated by hydrogen bonds between the sugar moiety and two lysine residues (K532 and K533). Targeted mutagenesis of these two residues confirmed their importance both for versicanase activity and inhibitor binding. This positively-charged cluster of ADAMTS-5 represents a previously unknown substrate-binding site (exosite) which is critical for substrate recognition and can therefore be targeted for the development of selective ADAMTS-5 inhibitors.

Challenges with matrix metalloproteinase inhibition and future drug discovery avenues

INTRODUCTION

Matrix metalloproteinases have been in the scope of pharmaceutical drug discovery for decades as promising targets for drug development. Until present, no modulator of the enzyme class survived clinical trials, all failing for various reasons. Nevertheless, the target family did not lose its attractiveness and there is ever more evidence that MMP modulators are likely to overcome the hurdles and result in successful clinical therapies.

AREAS COVERED

This review provides an overview of past efforts that were taken in the development of MMP inhibitors and insight into promising strategies that might enable drug discovery in the field in the future. Small molecule inhibitors as well as biomolecules are reviewed.

EXPERT OPINION

Despite the lack of successful clinical trials in the past, there is ongoing research in the field of MMP modulation, proving the target class has not lost its appeal to pharmaceutical research. With ever-growing insights from different scientific fields that shed light on previously unknown correlations, it is now time to use synergies deriving from biological knowledge, chemical structure generation, and clinical application to reach the ultimate goal of bringing MMP derived drugs on a broad front for the benefit of patients into therapeutic use.

Selective MMP-13 Inhibitors: Promising Agents for the Therapy of Osteoarthritis

Osteoarthritis (OA) is an age-related degenerative disease, which is characterized by chronic joint pain, inflammation and the damage of joint cartilage. At present, steroidal drugs and nonsteroidal anti-inflammatory drugs (NSAIDS), selective cyclooxygenase-2 (COX-2) inhibitors, are the first-line drugs for the treatment of OA. However, these drugs could lead to some cardiovascular side effects. Therefore, it is urgent to develop novel agents for the treatment of OA. Matrix metalloproteinase-13 (MMP-13), an important member of matrix metalloproteinases (MMPs) family, plays a vital role by degrading type II collagen in articular cartilage and bone in OA. It is noted that MMP-13 is specially expressed in the OA patients, and not in normal adults. In addition, broadspectrum MMP inhibitors could result in some painful and joint-stiffening side effects, called musculoskeletal syndrome (MSS) in the clinical trials. Thus, developing selective MMP-13 inhibitors is a potential strategy for the therapy of OA. In this review, we summarize the recent progress of selective MMP-13 inhibitors including two subfamilies, namely zinc-binding and non-zinc-binding selective MMP-13 inhibitors.

CyBy2: a strongly typed, purely functional framework for chemical data management

We present the development of CyBy², a versatile framework for chemical data management written in purely functional style in Scala, a modern multi-paradigm programming language. Together with the core libraries we provide a fully functional example implementation of a HTTP server together with a single page web client with powerful querying and visualization capabilities, providing essential functionality for people working in the field of organic and medicinal chemistry. The main focus of CyBy² are the diverse needs of different research groups in the field and therefore the flexibility required from the underlying data model. Techniques for writing type level specifications giving strong guarantees about the correctness of the implementation are described, together with the resulting gain in confidence during refactoring. Finally we talk about the advantages of using a single code base from which the server, the client and the software’s documentation pages are being generated. We conclude with a comparison with existing open source solutions. All code described in this article is published under version 3 of the GNU General Public License and available from GitHub including an example implementation of both backend and frontend together with documentation how to download and compile the software (available at https://github.com/stefan-hoeck/cyby2).

Approaching Target Selectivity by De Novo Drug Design

Introduction: The development of drug candidates with a defined selectivity profile and a unique molecular structure is of fundamental interest for drug discovery. In contrast to the costly screening of large substance libraries, the targeted de novo design of a drug by using structural information of either the biological target and/or structure-activity relationship data of active modulators offers an efficient and intellectually appealing alternative. Areas covered: This review provides an overview on the different techniques of de novo drug design (ligand-based drug design, structure-based drug design, and fragment-based drug design) and highlights successful examples of this targeted approach toward selective modulators of therapeutically relevant targets. Expert opinion: De novo drug design has established itself as a very efficient method for the development of potent and selective modulators for a variety of different biological target classes. The ever-growing wealth of structural data on therapeutic targets will certainly further enhance the importance of de novo design for the drug discovery process in the future. However, a consistent use of the terminology of de novo drug design in the scientific literature should be sought.

Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point

Natural products (NPs) present a privileged source of inspiration for chemical probe and drug design. Despite the biological pre-validation of the underlying molecular architectures and their relevance in drug discovery, the poor accessibility to NPs, complexity of the synthetic routes and scarce knowledge of their macromolecular counterparts in phenotypic screens still hinder their broader exploration. Cheminformatics algorithms now provide a powerful means of circumventing the abovementioned challenges and unlocking the full potential of NPs in a drug discovery context. Herein, I discuss recent advances in the computer-assisted design of NP mimics and how artificial intelligence may accelerate future NP-inspired molecular medicine.

Clinical Implications of Compounds Designed to Inhibit ECM-Modifying Metalloproteinases

Remodeling of the extracellular matrix (ECM) is crucial in development and homeostasis, but also has a significant role in disease progression. Two metalloproteinase families, the matrix metalloproteinases (MMPs) and a disintegrin and metalloproteases (ADAMs), participate in the remodeling of the ECM, either directly or through the liberation of growth factors and cell surface receptors. The correlation of MMP and ADAM activity to a variety of diseases has instigated numerous drug development programs. However, broad-based and Zn²⁺ -chelating MMP and ADAM inhibitors have fared poorly in the clinic. Selective MMP and ADAM inhibitors have been described recently based on (a) antibodies or antibody fragments or (b) small molecules designed to take advantage of protease secondary binding sites (exosites) or allosteric sites. Clinical trials have been undertaken with several of these inhibitors, while others are in advanced pre-clinical stages.

Development of a Non-Hydroxamate Dual Matrix Metalloproteinase (MMP)-7/-13 Inhibitor

Matrix metalloproteinase 7 (MMP-7) is a member of the MMP superfamily and is able to degrade extracellular matrix proteins such as casein, gelatin, fibronectin and proteoglycan. MMP-7 is a validated target for the development of small molecule drugs against cancer. MMP-13 is within the enzyme class the most efficient contributor to type II collagen degeneration and is a validated target in arthritis and cancer. We have developed the dual MMP-7/-13 inhibitor ZHAWOC6941 with IC₅₀-values of 2.2 μM (MMP-7) and 1.2 μM (MMP-13) that is selective over a broad range of MMP isoforms. It spares MMP-1, -2, -3, -8, -9, -12 and -14, making it a valuable modulator for targeted polypharmacology approaches.

Supporting the Identification of Novel Fragment-Based Positive Allosteric Modulators Using a Supervised Molecular Dynamics Approach: A Retrospective Analysis Considering the Human A2A Adenosine Receptor as a Key Example

Structure-driven fragment-based (SDFB) approaches have provided efficient methods for the identification of novel drug candidates. This strategy has been largely applied in discovering several pharmacological ligand classes, including enzyme inhibitors, receptor antagonists and, more recently, also allosteric (positive and negative) modulators. Recently, Siegal and collaborators reported an interesting study, performed on a detergent-solubilized StaR adenosine A_2A receptor, describing the existence of both fragment-like negative allosteric modulators (NAMs), and fragment-like positive allosteric modulators (PAMs). From this retrospective study, our results suggest that Supervised Molecular Dynamics (SuMD) simulations can support, on a reasonable time scale, the identification of fragment-like PAMs following their receptor recognition pathways and characterizing the possible allosteric binding sites.

Targeted Fluoro Positioning for the Discovery of a Potent and Highly Selective Matrix Metalloproteinase Inhibitor

The incorporation of fluorine atoms into functional molecules is of wide interest in synthetic organic chemistry as well as cognate disciplines. In particular, in medicinal chemistry, there is a strong desire to positively influence the physicochemical molecular properties of drug compounds by introducing fluorine into biologically active molecules. Here, we present targeted fluoro positioning as the key design principle of converting a weak matrix metalloproteinase‐13 (MMP‐13) inhibitor into a very potent (IC₅₀=6 nm) and highly selective (selectivity factors of >1000 over MMP‐1, 2, 3, 7, 8, 9, 10, 12, 14) inhibitor with excellent plasma and microsomal stability, and no binding to the hERG channel (hERG: human ether‐a‐go‐go related gene).

Fragment-to-Lead Medicinal Chemistry Publications in 2015

Fragment-based drug discovery (FBDD) is now well-established as a technology for generating new chemical leads and drugs. This Miniperspective provides a tabulated overview of the fragment-to-lead literature published in the year 2015, together with a commentary on trends observed across the FBDD field during this time. It is hoped that this tabulated summary will provide a useful point of reference for both FBDD practitioners and the wider medicinal chemistry community.

Targeting Antibiotic Resistance

Finding strategies against the development of antibiotic resistance is a major global challenge for the life sciences community and for public health. The past decades have seen a dramatic worldwide increase in human-pathogenic bacteria that are resistant to one or multiple antibiotics. More and more infections caused by resistant microorganisms fail to respond to conventional treatment, and in some cases, even last-resort antibiotics have lost their power. In addition, industry pipelines for the development of novel antibiotics have run dry over the past decades. A recent world health day by the World Health Organization titled "Combat drug resistance: no action today means no cure tomorrow" triggered an increase in research activity, and several promising strategies have been developed to restore treatment options against infections by resistant bacterial pathogens.

Molecular Recognition of the Catalytic Zinc(II) Ion in MMP-13: Structure-Based Evolution of an Allosteric Inhibitor to Dual Binding Mode Inhibitors with Improved Lipophilic Ligand Efficiencies

Matrix metalloproteinases (MMPs) are a class of zinc dependent endopeptidases which play a crucial role in a multitude of severe diseases such as cancer and osteoarthritis. We employed MMP-13 as the target enzyme for the structure-based design and synthesis of inhibitors able to recognize the catalytic zinc ion in addition to an allosteric binding site in order to increase the affinity of the ligand. Guided by molecular modeling, we optimized an initial allosteric inhibitor by addition of linker fragments and weak zinc binders for recognition of the catalytic center. Furthermore we improved the lipophilic ligand efficiency (LLE) of the initial inhibitor by adding appropriate zinc binding fragments to lower the clogP values of the inhibitors, while maintaining their potency. All synthesized inhibitors showed elevated affinity compared to the initial hit, also most of the novel inhibitors displayed better LLE. Derivatives with carboxylic acids as the zinc binding fragments turned out to be the most potent inhibitors (compound 3 (ZHAWOC5077): IC50 = 134 nM) whereas acyl sulfonamides showed the best lipophilic ligand efficiencies (compound 18 (ZHAWOC5135): LLE = 2.91).

De Novo Design at the Edge of Chaos

Computational medicinal chemistry offers viable strategies for finding, characterizing, and optimizing innovative pharmacologically active compounds. Technological advances in both computer hardware and software as well as biological chemistry have enabled a renaissance of computer-assisted "de novo" design of molecules with desired pharmacological properties. Here, we present our current perspective on the concept of automated molecule generation by highlighting chemocentric methods that may capture druglike chemical space, consider ligand promiscuity for hit and lead finding, and provide fresh ideas for the rational design of customized screening of compound libraries.

Capturing Biological Activity in Natural Product Fragments by Chemical Synthesis

Natural products have had an immense influence on science and have directly led to the introduction of many drugs. Organic chemistry, and its unique ability to tailor natural products through synthesis, provides an extraordinary approach to unlock the full potential of natural products. In this Review, an approach based on natural product derived fragments is presented that can successfully address some of the current challenges in drug discovery. These fragments often display significantly reduced molecular weights, reduced structural complexity, a reduced number of synthetic steps, while retaining or even improving key biological parameters such as potency or selectivity. Examples from various stages of the drug development process up to the clinic are presented. In addition, this process can be leveraged by recent developments such as genome mining, antibody–drug conjugates, and computational approaches. All these concepts have the potential to identify the next generation of drug candidates inspired by natural products.

A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets

Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for “growing” the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies.