Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Promising and Economical Small Molecule Inhibitors of Complement Factor C1s

C1s targeting antibodies inhibit the growth of cutaneous squamous carcinoma cells

Cutaneous squamous cell carcinoma (cSCC) is the most common metastatic skin cancer. The incidence of cSCC is increasing globally and the prognosis of metastatic disease is poor. Currently there are no specific targeted therapies for advanced or metastatic cSCC. We have previously shown abundant expression of the complement classical pathway C1 complex components, serine proteases C1r and C1s in tumor cells in invasive cSCCs in vivo, whereas the expression of C1r and C1s was lower in cSCCs in situ, actinic keratoses and in normal skin. We have also shown that knockdown of C1s expression results in decreased viability and growth of cSCC cells by promoting apoptosis both in culture and in vivo. Here, we have studied the effect of specific IgG2a mouse monoclonal antibodies TNT003 and TNT005 targeting human C1s in five primary non-metastatic and three metastatic cSCC cell lines that show intracellular expression of C1s and secretion of C1s into the cell culture media. Treatment of cSCC cells with TNT003 and TNT005 significantly inhibited their growth and viability and promoted apoptosis of cSCC cells. These data indicate that TNT003 and TNT005 inhibit cSCC cell growth in culture and warrant further investigation of C1s targeted inhibition in additional in vitro and in vivo models of cSCC.

Global Trends in Research of Antimicrobial Peptides for the Treatment of Drug-Resistant Bacteria from 1995 to 2021: A Bibliometric Analysis

Background

Antimicrobial peptides (AMPs) can act on the bacterial cell membrane to play an antibacterial role in types of drug-resistant bacteria. Therefore, AMPs have attracted more and more attention in the treatment of drug-resistant bacteria.

Methods

Bibliometric analysis was employed to sort out the development and trends in the research of AMPs in the treatment of drug-resistant bacteria and map the knowledge structure for scholars.

Results

Since 2010, the publications and citations in this field have exploded, indicating a growing global interest in the field of AMPs for the treatment of drug-resistant bacteria. And as major countries in this field, China and the USA had conducted very in-depth exchanges and cooperation, which had injected a steady stream of impetus into this field. Both old and new scholars have made efforts, and related fields have developed rapidly, especially in the synthesis and improvement of novel AMPs. In recent years, research directions in the field of AMPs for the treatment of drug-resistant bacteria gradually focused on the practical application, optimization of drug delivery mode, optimization of synthesis mode, screening of new AMPs and other fields, indicating that the relevant research results of AMPs for the treatment of drug-resistant bacteria had entered the actual clinical stage, with higher practical significance.

Conclusion

The research history, global research status, future research hotspots, and trends of the research of AMPs in the treatment of drug-resistant bacteria were discussed in depth in this study, which can provide research references and inspiration for researchers inside and outside the related field.

Discovery of a Novel Series of Potent, Selective, Orally Available, and Brain-Penetrable C1s Inhibitors for Modulation of the Complement Pathway

A novel series of non-amidine-based C1s inhibitors have been explored. Starting from high-throughput screening hit 3, isoquinoline was replaced with 1-aminophthalazine to enhance C1s inhibitory activity while exhibiting good selectivity against other serine proteases. We first disclose a crystal structure of a complex of C1s and a small-molecule inhibitor (4e), which guided structure-based optimization around the S2 and S3 sites to further enhance C1s inhibitory activity by over 300-fold. Improvement of membrane permeability by incorporation of fluorine at the 8-position of 1-aminophthalazine led to identification of (R)-8 as a potent, selective, orally available, and brain-penetrable C1s inhibitor. (R)-8 significantly inhibited membrane attack complex formation induced by human serum in a dose-dependent manner in an in vitro assay system, proving that selective C1s inhibition blocked the classical complement pathway effectively. As a result, (R)-8 emerged as a valuable tool compound for both in vitro and in vivo assessment.

Peptide–Polymer Conjugates: A Promising Therapeutic Solution for Drug-Resistant Bacteria

By 2050, it is estimated that 10 million people will die of drug-resistant bacterial infection caused by antibiotic abuse. Antimicrobial peptide (AMP) is widely used to prevent such circumstances, for the positively charged AMPs can kill drug-resistant bacteria by destroying negatively charged bacterial cell membrane, and has excellent antibacterial efficiency and low drug resistance. However, due to the defects in low in vivo stability, easy degradation, and certain cytotoxicity, its practical clinical application is limited. The emergence of peptide–polymer conjugates (PPC) helps AMPs overcome these shortcomings. By combining with functional polymers, the positive charge of AMPs is partially shielded, and its stability and water solubility are improved, so as to prolong the in vivo circulation time of AMPs and reduce its cytotoxicity. At the same time, the self-assembly ability of PPC enables it to assemble into different nanostructures to undertake specific antibacterial tasks. At present, PPC is mainly used in wound dressing, bone tissue repair, antibacterial coating of medical devices, nerve repair, tumor treatment, and oral health maintenance. In this study, we summarize the structure, synthesis methods, and the clinical applications of PPC, so as to present the current challenges and discuss the future prospects of antibacterial therapeutic materials.

Complement C1s as a diagnostic marker and therapeutic target: Progress and propective

The molecules of the complement system connect the effectors of innate and adaptive immunity and play critical roles in maintaining homeostasis. Among them, the C1 complex, composed of C1q, C1r, and C1s (C1qr₂s₂), is the initiator of the classical complement activation pathway. While deficiency of C1s is associated with early-onset systemic lupus erythematosus and increased susceptibility to bacteria infections, the gain-of- function variants of C1r and C1s may lead to periodontal Ehlers Danlos syndrome. As C1s is activated under various pathological conditions and associated with inflammation, autoimmunity, and cancer development, it is becoming an informative biomarker for the diagnosis and treatment of a variety of diseases. Thus, more sensitive and convenient methods for assessing the level as well as activity of C1s in clinic samples are highly desirable. Meanwhile, a number of small molecules, peptides, and monoclonal antibodies targeting C1s have been developed. Some of them are being evaluated in clinical trials and one of the antibodies has been approved by US FDA for the treatment of cold agglutinin disease, an autoimmune hemolytic anemia. In this review, we will summarize the biological properties of C1s, its association with development and diagnosis of diseases, and recent progress in developing drugs targeting C1s. These progress illustrate that the C1s molecule is an effective biomarker and promising drug target.

A review on machine learning approaches and trends in drug discovery

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

PubChem in 2021: new data content and improved web interfaces

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves the scientific community as well as the general public, with millions of unique users per month. In the past two years, PubChem made substantial improvements. Data from more than 100 new data sources were added to PubChem, including chemical-literature links from Thieme Chemistry, chemical and physical property links from SpringerMaterials, and patent links from the World Intellectual Properties Organization (WIPO). PubChem's homepage and individual record pages were updated to help users find desired information faster. This update involved a data model change for the data objects used by these pages as well as by programmatic users. Several new services were introduced, including the PubChem Periodic Table and Element pages, Pathway pages, and Knowledge panels. Additionally, in response to the coronavirus disease 2019 (COVID-19) outbreak, PubChem created a special data collection that contains PubChem data related to COVID-19 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

PubChem in 2021: new data content and improved web interfaces

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves the scientific community as well as the general public, with millions of unique users per month. In the past two years, PubChem made substantial improvements. Data from more than 100 new data sources were added to PubChem, including chemical-literature links from Thieme Chemistry, chemical and physical property links from SpringerMaterials, and patent links from the World Intellectual Properties Organization (WIPO). PubChem's homepage and individual record pages were updated to help users find desired information faster. This update involved a data model change for the data objects used by these pages as well as by programmatic users. Several new services were introduced, including the PubChem Periodic Table and Element pages, Pathway pages, and Knowledge panels. Additionally, in response to the coronavirus disease 2019 (COVID-19) outbreak, PubChem created a special data collection that contains PubChem data related to COVID-19 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Design and Selection of Novel C1s Inhibitors by In Silico and In Vitro Approaches

The complement system is associated with various diseases such as inflammation or auto-immune diseases. Complement-targeted drugs could provide novel therapeutic intervention against the above diseases. C1s, a serine protease, plays an important role in the CS and could be an attractive target since it blocks the system at an early stage of the complement cascade. Designing C1 inhibitors is particularly challenging since known inhibitors are restricted to a narrow bioactive chemical space in addition selectivity over other serine proteases is an important requirement. The typical architecture of a small molecule inhibitor of C1s contains an amidine (or guanidine) residue, however, the discovery of non-amidine inhibitors might have high value, particularly if novel chemotypes and/or compounds displaying improved selectivity are identified. We applied various virtual screening approaches to identify C1s focused libraries that lack the amidine/guanidine functionalities, then the in silico generated libraries were evaluated by in vitro biological assays. While 3D structure-based methods were not suitable for virtual screening of C1s inhibitors, and a 2D similarity search did not lead to novel chemotypes, pharmacophore model generation allowed us to identify two novel chemotypes with submicromolar activities. In three screening rounds we tested altogether 89 compounds and identified 20 hit compounds (<10 μM activities; overall hit rate: 22.5%). The highest activity determined was 12 nM (1,2,4-triazole), while for the newly identified chemotypes (1,3-benzoxazin-4-one and thieno[2,3-d][1,3]oxazin-4-one) it was 241 nM and 549 nM, respectively.

Machine Learning for Molecular Modelling in Drug Design

Machine learning (ML) has become a crucial component of early drug discovery. This researcharea has been fueled by two main factors [...].

Identifying de-NEDDylation inhibitors: Virtual high-throughput screens targeting SENP8

Protein modification can have far-reaching effects. NEDDylation, a protein modification process with the protein NEDD8, stabilizes and modifies how the targeted protein interacts with other proteins. Its role in system regulation makes it a prime therapeutic target, and virtual high-throughput screening has already identified new NEDD8 inhibitors. SENP8 matures the NEDD8 proenzyme into the active form and regulates NEDDylation by removing NEDD8 from over-NEDDylated proteins. In this work, SENP8 inhibitor candidates were identified in two rounds of virtual high-throughput screening. Of the ten candidates identified in the first round of screening, four were active in validation experiments to yield an experimental hit rate of 40%. Of the five candidates identified in the second round of screening, one was active in validation experiments to yield an experimental hit rate of 20%. Results indicate virtual high-throughput screening improved hit rates over traditional high-throughput screening. The SENP8 inhibitor candidates can be used to interrogate the NEDDylation regulation mechanism.

Virtual high-throughput screens identifying hPK-M2 inhibitors: Exploration of model extrapolation

Glycolysis with PK-M2 occurs typically in anaerobic conditions and atypically in aerobic conditions, which is known as the Warburg effect. The Warburg effect is found in many oncogenic situations and is believed to provide energy and biomass for oncogenesis to persist. The work presented targets human PK-M2 (hPK-M2) in a virtual high-throughput screen to identify new inhibitors and leads for further study. In the initial screen, one of the 12 candidates selected for experimental validation showed biological activity (hit-rate = 8.13%). In the second screen with retrained models, six of 11 candidates selected for experimental validation showed biological activity (hit-rate: 54.5%). Additionally, four different scaffolds were identified for further analysis when examining the tested candidates and compounds in the training data. Finally, extrapolation was necessary to identify a sufficient number of candidates to test in the second screen. Examination of the results suggested stepwise extrapolation to maximize efficiency.