We are MedChem: The Frontiers in Medicinal Chemistry 2024

The Frontiers in Medicinal Chemistry (FiMC) is the largest international Medicinal Chemistry conference in Germany and took place from March 17th to 20th 2024 in Munich. Co-organized by the Division of Medicinal Chemistry of the German Chemical Society (Gesellschaft Deutscher Chemiker; GDCh) and the Division of Pharmaceutical and Medicinal Chemistry of the German Pharmaceutical Society (Deutsche Pharmazeutische Gesellschaft; DPhG), and supported by a local organizing committee from the Ludwigs-Maximilians-University Munich headed by Daniel Merk, the meeting brought together approximately 225 participants from 20 countries. The outstanding program of the four-day conference included 40 lectures by leading scientists from industry and academia as well as early career investigators. Moreover, 100 posters were presented in two highly interactive poster sessions.

PICKAPEP: An application for parameter calculation and visualization of cyclized and modified peptidomimetics

The interest in peptides and especially in peptidomimetic structures has risen enormously in the past few years. Novel modification strategies including nonnatural amino acids, sophisticated cyclization strategies, and side chain modifications to improve the pharmacokinetic properties of peptides are continuously arising. However, a calculator tool accompanying the current development in peptide sciences towards modified peptides is missing. Herein, we present the application PICKAPEP, enabling the virtual construction and visualization of peptidomimetics ranging from well-known cyclized and modified peptides such as ciclosporin A up to fully self-designed peptide-based structures with custom amino acids. Calculated parameters include the molecular weight, the water-octanol partition coefficient, the topological polar surface area, the number of rotatable bonds, and the peptide SMILES code. To our knowledge, PICKAPEP is the first tool allowing users to add custom amino acids as building blocks and also the only tool giving the possibility to process large peptide libraries and calculate parameters for multiple peptides at once. We believe that PICKAPEP will support peptide researchers in their work and will find wide application in current as well as future peptide drug development processes. PICKAPEP is available open source for Windows and Mac operating systems (https://www.research-collection.ethz.ch/handle/20.500.11850/681174).

Discovery of Highly Bioactive Peptides through Hierarchical Structural Information and Molecular Dynamics Simulations

Peptide drugs play an essential role in modern therapeutics, but the computational design of these molecules is hindered by several challenges. Traditional methods like molecular docking and molecular dynamics (MD) simulation, as well as recent deep learning approaches, often face limitations related to computational resource demands, complex binding affinity assessments, extensive data requirements, and poor model interpretability. Here, we introduce PepHiRe, an innovative methodology that utilizes the hierarchical structural information in peptide sequences and employs a novel strategy called Ladderpath, rooted in algorithmic information theory, to rapidly generate and enhance the efficiency and clarity of novel peptide design. We applied PepHiRe to develop BH3-like peptide inhibitors targeting myeloid cell leukemia-1, a protein associated with various cancers. By analyzing just eight known bioactive BH3 peptide sequences, PepHiRe effectively derived a hierarchy of subsequences used to create new BH3-like peptides. These peptides underwent screening through MD simulations, leading to the selection of five candidates for synthesis and subsequent in vitro testing. Experimental results demonstrated that these five peptides possess high inhibitory activity, with IC50 values ranging from 28.13 ± 7.93 to 167.42 ± 22.15 nM. Our study explores a white-box model driven technique and a structured screening pipeline for identifying and generating novel peptides with potential bioactivity.

Identification of Anticancer Peptides from the Genome of Candida albicans: in Silico Screening, in Vitro and in Vivo Validations

Anticancer peptides (ACPs) are promising future therapeutics, but their experimental discovery remains time-consuming and costly. To accelerate the discovery process, we propose a computational screening workflow to identify, filter, and prioritize peptide sequences based on predicted class probability, antitumor activity, and toxicity. The workflow was applied to identify novel ACPs with potent activity against colorectal cancer from the genome sequences of Candida albicans. As a result, four candidates were identified and validated in the HCT116 colon cancer cell line. Among them, PCa1 and PCa2 emerged as the most potent, displaying IC50 values of 3.75 and 56.06 μM, respectively, and demonstrating a 4-fold selectivity for cancer cells over normal cells. In the colon xenograft nude mice model, the administration of both peptides resulted in substantial inhibition of tumor growth without causing significant adverse effects. In conclusion, this work not only contributes a proven computational workflow for ACP discovery but also introduces two peptides, PCa1 and PCa2, as promising candidates poised for further development as targeted therapies for colon cancer. The method as a web service is available at https://app.cbbio.online/acpep/home and the source code at https://github.com/cartercheong/AcPEP_classification.git.

Rational design of a new short anticancer peptide with good potential for cancer treatment

Anticancer peptides (ACPs) have regarded as a new generation of promising antitumor drugs due to the unique mode of action. The main challenge is to develop potential anticancer peptides with satisfied antitumor activity and low toxicity. Here, a series of new α-helical anticancer peptides were designed and synthesized based on the regular repeat motif KLLK. The optimal peptides 14E and 14Aad were successfully derived from the new short α-helical peptide KL-8. Our results demonstrated that 14E and 14Aad had good antitumor activity and low toxicity, exhibiting excellent selectivity index. This result highlighted that the desirable modification position and appropriate hydrophobic side-chain structure of acidic amino acids played critical roles in regulating the antitumor activity/toxicity of new peptides. Further studies indicated that they could induce tumor cell death via the multiple actions of efficient membrane disruption and intracellular mechanisms, displaying apparent superiority in combination with PTX. In addition, the new peptides 14E and 14Aad showed excellent antitumor efficacy in vivo and low toxicity in mice compared to KL-8 and PTX. Particularly, 14Aad with the longer side chain at the 14th site exhibited the best therapeutic performance. In conclusion, our work provided a new avenue to develop promising anticancer peptides with good selectivity for tumor therapy.

Stimuli-responsive peptide assemblies: Design, self-assembly, modulation, and biomedical applications

Peptide molecules have design flexibility, self-assembly ability, high biocompatibility, good biodegradability, and easy functionalization, which promote their applications as versatile biomaterials for tissue engineering and biomedicine. In addition, the functionalization of self-assembled peptide nanomaterials with other additive components enhances their stimuli-responsive functions, promoting function-specific applications that induced by both internal and external stimulations. In this review, we demonstrate recent advance in the peptide molecular design, self-assembly, functional tailoring, and biomedical applications of peptide-based nanomaterials. The strategies on the design and synthesis of single, dual, and multiple stimuli-responsive peptide-based nanomaterials with various dimensions are analyzed, and the functional regulation of peptide nanomaterials with active components such as metal/metal oxide, DNA/RNA, polysaccharides, photosensitizers, 2D materials, and others are discussed. In addition, the designed peptide-based nanomaterials with temperature-, pH-, ion-, light-, enzyme-, and ROS-responsive abilities for drug delivery, bioimaging, cancer therapy, gene therapy, antibacterial, as well as wound healing and dressing applications are presented and discussed. This comprehensive review provides detailed methodologies and advanced techniques on the synthesis of peptide nanomaterials from molecular biology, materials science, and nanotechnology, which will guide and inspire the molecular level design of peptides with specific and multiple functions for function-specific applications.

Recent Advances in the Field of Amino Acid-Conjugated Aminoferrocenes-A Personal Perspective

The development of turn-based inhibitors of protein-protein interactions has attracted considerable attention in medicinal chemistry. Our group has synthesized a series of peptides derived from an amino-functionalized ferrocene to investigate their potential to mimic protein turn structures. Detailed DFT and spectroscopic studies (IR, NMR, CD) have shown that, for peptides, the backbone chirality and bulkiness of the amino acid side chains determine the hydrogen-bond pattern, allowing tuning of the size of the preferred hydrogen-bonded ring in turn-folded structures. However, their biological potential is more dependent on their lipophilicity. In addition, our pioneering work on the chiroptical properties of aminoferrocene-containing peptides enables the correlation of their geometry with the sign of the CD signal in the absorption region of the ferrocene chromophore. These studies have opened up the possibility of using aminoferrocene and its derivatives as chirooptical probes for the determination of various chirality elements, such as the central chirality of amino acids and the helicity of peptide sequences.

The Scent of Change: Sustainable Fragrances Through Industrial Biotechnology

Current environmental and safety considerations urge innovation to address the need for sustainable high-value chemicals that are embraced by consumers. This review discusses the concept of sustainable fragrances, as high-value, everyday and everywhere chemicals. Current and emerging technologies represent an opportunity to produce fragrances in an environmentally and socially responsible way. Biotechnology, including fermentation, biocatalysis, and genetic engineering, has the potential to reduce the environmental footprint of fragrance production while maintaining quality and consistency. Computational and in silico methods, including machine learning (ML), are also likely to augment the capabilities of sustainable fragrance production. Continued innovation and collaboration will be crucial to the future of sustainable fragrances, with a focus on developing novel sustainable ingredients, as well as ethical sourcing practices.

One New Acid-Activated Hybrid Anticancer Peptide by Coupling with a Desirable pH-Sensitive Anionic Partner Peptide

Anticancer peptides (ACPs) are promising antitumor resources, and developing acid-activated ACPs as more effective and selective antitumor drugs would represent new progress in cancer therapy. In this study, we designed a new class of acid-activated hybrid peptides LK-LE by altering the charge shielding position of the anionic binding partner LE based on the cationic ACP LK and investigated their pH response, cytotoxic activity, and serum stability, in hoping to achieve a desirable acid-activatable ACP. As expected, the obtained hybrid peptides could be activated and exhibit a remarkable antitumor activity by rapid membrane disruption at acidic pH, whereas its killing activity could be alleviated at normal pH, showing a significant pH response compared with LK. Importantly, this study found that the peptide LK-LE3 with the charge shielding in the N-terminal of LK displayed notably low cytotoxicity and more stability, demonstrating that the position of charge masking is extremely important for the improvement of peptide toxicity and stability. In short, our work opens a new avenue to design promising acid-activated ACPs as potential targeting agents for cancer treatment.

Antimicrobial peptides with anticancer activity: Today status, trends and their computational design

Some antimicrobial peptides have been shown to be able to inhibit the proliferation of cancer cell lines. Various strategies for treating cancers with active peptides have been pursued. According to the reports, anticancer peptides are important therapeutic peptides, which can act through two distinct pathways: they either just create pores in the cell membrane, or they have a vital intracellular target. In this review, publications up to Sep. 2021 had extracted form Scopus and PubMed using "antimicrobial peptide" and "anticancer peptide" as keywords. In second step, "computational design" related publications extracted. Among publications, those have similar scopes were classified and selected based on mechanisms of action and application. In this review, the most recent advances in the field of antimicrobial peptides with anti-cancer activities have been summarized. Freely available webservers such as AntiCP, ACPP, iACP, iACP-GAEnsC, ACPred are discussed here. In conclusion, despite some limitations of ACPs such as production cost and challenges, short half-life and toxicity on normal cells, the beneficial properties of AMPs make some of them good therapeutic agents for cancer therapy. Towards designing novel ACPs, the computational methods have substantial position and have been used progressively, today.

Emerging Computational Approaches for Antimicrobial Peptide Discovery

In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.

A story of peptides, lipophilicity and chromatography - back and forth in time

Peptides, as part of the beyond the rule of 5 (bRo5) chemical space, represent a unique class of pharmaceutical compounds. Because of their exceptional position in the chemical space between traditional small molecules (molecular weight (MW) < 500 Da) and large therapeutic proteins (MW > 5000 Da), peptides became promising candidates for targeting challenging binding sites, including even targets traditionally considered as undruggable - e.g. intracellular protein-protein interactions. However, basic knowledge about physicochemical properties that are important for a drug to be membrane permeable is missing but would enhance the drug discovery process of bRo5 molecules. Consequently, there is a demand for quick and simple lipophilicity determination methods for peptides. In comparison to the traditional lipophilicity determination methods via shake flask and in silico prediction, chromatography-based methods could have multiple benefits such as the requirement of low analyte amount, insensitivity to impurities and high throughput. Herein we elucidate the role of peptide lipophilicity and different lipophilicity values. Further, we summarize peptide analysis via common chromatographic techniques, in specific reversed phase liquid chromatography, hydrophilic interaction liquid chromatography and supercritical fluid chromatography and their role in drug discovery and development process.

Integrated Design of a Membrane-Lytic Peptide-Based Intravenous Nanotherapeutic Suppresses Triple-Negative Breast Cancer

Membrane-lytic peptides offer broad synthetic flexibilities and design potential to the arsenal of anticancer therapeutics, which can be limited by cytotoxicity to noncancerous cells and induction of drug resistance via stress-induced mutagenesis. Despite continued research efforts on membrane-perforating peptides for antimicrobial applications, success in anticancer peptide therapeutics remains elusive given the muted distinction between cancerous and normal cell membranes and the challenge of peptide degradation and neutralization upon intravenous delivery. Using triple-negative breast cancer as a model, the authors report the development of a new class of anticancer peptides. Through function-conserving mutations, the authors achieved cancer cell selective membrane perforation, with leads exhibiting a 200-fold selectivity over non-cancerogenic cells and superior cytotoxicity over doxorubicin against breast cancer tumorspheres. Upon continuous exposure to the anticancer peptides at growth-arresting concentrations, cancer cells do not exhibit resistance phenotype, frequently observed under chemotherapeutic treatment. The authors further demonstrate efficient encapsulation of the anticancer peptides in 20 nm polymeric nanocarriers, which possess high tolerability and lead to effective tumor growth inhibition in a mouse model of MDA-MB-231 triple-negative breast cancer. This work demonstrates a multidisciplinary approach for enabling translationally relevant membrane-lytic peptides in oncology, opening up a vast chemical repertoire to the arms race against cancer.

Peptide-Based Sensing, Logic Computing, and Information Security on the Antimonene Platform

Peptides have higher information density than DNA and equivalent molecular recognition ability and durability. However, there are currently no reports on the comprehensive use of peptides' recognition ability and structural diversity for sensing, logic computing, information coding, and protection. Herein, we, for the first time, demonstrate peptide-based sensing, logic computing, and information security on the antimonene platform. The molecular recognition capability and structural diversity (amino acid sequence) of peptides (Pb²⁺-binding peptide DHHTQQHD as a model) adsorbed on the antimonene universal fluorescence quenching platform were comprehensively utilized to sense targets (Pb²⁺) and give a response (fluorescence turn-on) and then to encode, encrypt, and hide information. Fluorescently labeled peptides used as the recognition probe and the information carrier were quenched and hidden by the large-plane two-dimensional material antimonene and specifically bound by Pb²⁺ as the stego key, resulting in fluorescence recovery. The above interaction and signal change can be considered as a peptide-based sensing and steganographic process to further implement quantitative detection of Pb²⁺, complex logic operation, information coding, encrypting, and hiding using a peptide sequence and the binary conversion of its selectivity. This research provides a basic paradigm for the construction of a molecular sensing and informatization platform and will inspire the development of biopolymer-based molecular information technology (processing, communication, control, security).

Genetic Algorithms Applied to Thermodynamic Rational Design of Mimetic Antibodies Based on the GB1 Domain of Streptococcal Protein G: An Atomistic Simulation Study

The development of mimetic antibodies (MA) capable of combining the high affinity and selectivity of antibodies with the small size of the peptides has enormous potential for applications in current biotechnology. In this work, we demonstrate that in silico MA design is possible through genetic algorithms (GA) developed from shell scripts capable of combining software commonly used for atomistic simulation. Our results demonstrate that, using the GB1 domain of the streptococcal G protein as a model, it is possible to optimize the molecular recognition capacity of a large MA population in a few generations. In the first case, GA was able to generate 10 MA with binding free energy (BFE) less than the vascular endothelial cell growth factor conjugated with the fms-type tyrosine kinase receptor. In the second case, it generated 13 MA with BFE less than that of the hepatitis C-E2 viral envelope conjugate with the antibody. Through the GA developed in this work, we demonstrate the use of a new protocol, capable of guiding experimental methods for the design of bioactive peptides that can assist in the development of new therapeutic molecules.

Simultaneous Quantification of Commonly Used Counter Ions in Peptides and Active Pharmaceutical Ingredients by Mixed Mode Chromatography and Evaporative Light Scattering Detection

In academia and industry, the analysis of counter ions in small molecules and synthetic peptides represents a great challenge. Due to the frequent use of salt forms and the application of a wider range of counter ions in pharmaceutically used substances, simple and generic methods for quantification are required. Especially, the analysis of trifluoracetic acid (TFA) in synthetic peptides is of high interest. Quantification of TFA is needed to assess the content and safety of synthetic peptides and for the interpretation of functional assay results, respectively. In here, a full quantitative mixed mode high performance liquid chromatography based method coupled to evaporative light scattering detection is presented. Finally, 14 positively and negatively charged counter ions were simultaneously quantified within 30 minutes. The method was validated in terms of specificity, accuracy, precision, limit of quantification, sample stability and carry over as proposed by the International Council of Harmonization. In order to prove the applicability of the procedure, small molecules reference substances and synthetic peptides were analyzed, respectively. The obtained results indicated a successful determination of counter ions in small molecules and differences to expected concentrations of prepared peptide solutions. Furthermore, an unexpectedly high content of sodium was observed for synthetic peptides.

Annual review of LSD1/KDM1A inhibitors in 2020

Lysine-specific demethylase 1 (LSD1/KDM1A) has emerged as a promising target for the discovery of specific inhibitors as antitumor drugs. Based on the source of compounds, all LSD1 inhibitors in this review are divided into two categories: natural LSD1 inhibitors and synthetic LSD1 inhibitors. This review highlights the research progress of LSD1 inhibitors with the potential to treat cancer covering articles published in 2020. Design strategies, structure-activity relationships, co-crystal structure analysis and action mechanisms are also highlighted.

New designed pH-responsive histidine-rich peptides with antitumor activity

Anticancer peptides have received widespread attention as alternative antitumor therapeutics due to their unique action mode. However, the systemic toxicity hampers their successful utilisation in tumour therapy. Here, the tumour acidic environment was used as a trigger to design a series of histidine-rich peptides by optimising the distribution of histidine and leucine based on the amphiphilic peptide LK, in hoping to achieve desirable acid-activate anticancer peptides. Among all the obtained peptides, L9H5-1 showed enhanced antitumor activity at acidic pH concomitant with low toxicity at normal pH, exhibiting excellent pH-response. At acidic pH, protonated L9H5-1 could rapidly kill tumour cells by efficient membrane disruption as evidenced by in vitro experiments, including increasing intracellular PI uptake and LDH release, dramatic membrane damage and increase of later apoptotic/necrotic cells. Moreover, no cell cycle arrest was observed after treated with L9H5-1. Interestingly, this study found that the new peptides with the same number of histidines and leucines displayed different pH-dependent antitumor activity, indicating that the position of amino acid alteration is extremely important for the design of acid-activated histidine-rich peptides. In short, our work provides a new avenue to develop new acid-activated anticancer peptides as promising antitumor drugs with high efficiency and good selectivity.

How Computational Chemistry and Drug Delivery Techniques Can Support the Development of New Anticancer Drugs

The early and late development of new anticancer drugs, small molecules or peptides can be slowed down by some issues such as poor selectivity for the target or poor ADME properties. Computer-aided drug design (CADD) and target drug delivery (TDD) techniques, although apparently far from each other, are two research fields that can give a significant contribution to overcome these problems. Their combination may provide mechanistic understanding resulting in a synergy that makes possible the rational design of novel anticancer based therapies. Herein, we aim to discuss selected applications, some also from our research experience, in the fields of anticancer small organic drugs and peptides.

Mitochondrion-Targeting Peptides and Peptidomimetics: Recent Progress and Design Principles

Mitochondria are multifunctional subcellular organelles whose operations encompass energy production, signal transduction, and metabolic regulation. Given their wide range of roles, they have been studied extensively as a potential therapeutic target for the treatment of various diseases, including cancer, diabetes, and neurodegenerative diseases. Mitochondrion-mediated pathways have been identified as promising targets in the context of these diseases. However, the delivery of specific probes and drugs to the mitochondria is one of the major problems that remains to be solved. Over the past decade, much effort has been devoted to developing mitochondrion-targeted delivery methods based on the membrane characteristics and the protein import machinery of mitochondria. While various methods utilizing small molecules to polymeric particles have been introduced, it is notable that many of these compounds share common structural elements and physicochemical properties for optimal selectivity and efficiency. In this Perspective, we will review the most recently developed mitochondrion-targeting peptides and peptidomimetics to outline the key aspects of structural requirements and design principles. We will also discuss successful and potential applications of mitochondrial delivery to assess opportunities and challenges in the targeting of mitochondria.

Algorithm-supported, mass and sequence diversity-oriented random peptide library design

Random peptide libraries that cover large search spaces are often used for the discovery of new binders, even when the target is unknown. To ensure an accurate population representation, there is a tendency to use large libraries. However, parameters such as the synthesis scale, the number of library members, the sequence deconvolution and peptide structure elucidation, are challenging when increasing the library size. To tackle these challenges, we propose an algorithm-supported approach to peptide library design based on molecular mass and amino acid diversity. The aim is to simplify the tedious permutation identification in complex mixtures, when mass spectrometry is used, by avoiding mass redundancy. For this purpose, we applied multi (two- and three-)-objective genetic algorithms to discriminate between library members based on defined parameters. The optimizations led to diverse random libraries by maximizing the number of amino acid permutations and minimizing the mass and/or sequence overlapping. The algorithm-suggested designs offer to the user a choice of appropriate compromise solutions depending on the experimental needs. This implies that diversity rather than library size is the key element when designing peptide libraries for the discovery of potential novel biologically active peptides.