Peptides are compounds formed by amino acids linked through peptide bonds, typically having molecular weights between small molecule drugs and large molecular biologics, offering a balance of high activity and selectivity. Small molecule drugs are the most widely used and are commonly employed in the treatment of cancer, cardiovascular diseases, and immune system disorders. Large molecule biologics, such as monoclonal antibodies, are mainly used for treating immune system diseases and cancers. Peptide drugs, due to their natural activity and specificity, have demonstrated unique advantages in areas such as infectious diseases, immune system disorders, and metabolic diseases, showing vast potential for application. Peptide drugs come in various forms, including natural peptides (e.g., insulin), synthetic peptides (e.g., liraglutide), and modified peptides (e.g., pegylated interferon). With continuous advancements in synthesis technologies, the scope of peptide drug applications continues to expand and has become an essential focus of modern drug development.
Cyclic peptides are a unique class of peptide compounds in which the amino acid sequence forms a closed ring structure by linking the ends or side chains. Compared to linear peptides, Cyclic peptides typically exhibit higher biological activity, structural stability, and permeability. The cyclization of the peptide limits its conformational flexibility, reducing entropy loss when binding to the target, significantly enhancing binding affinity. Additionally, the rigid structure of Cyclic peptides allows for more precise shape and charge matching during interactions with the target, leading to higher selectivity. The limited flexibility of Cyclic peptides also hinders their binding to the active sites of proteases, providing enhanced resistance to enzymatic degradation. For example, in the intestine, trypsin and in the bloodstream, coagulation proteases require substrates to adapt to specific active sites to optimize the relative positioning of the substrate and catalytic site and stabilize the transition state of the reaction. Furthermore, cyclization of the terminal amino and carboxyl groups can protect the peptide from degradation by exopeptidases, resulting in Cyclic peptides having a longer half-life and higher stability in vivo. Cyclic peptides can promote the formation of intramolecular hydrogen bonds, reducing the solvation effect of polar groups and hiding part of the polar surface. This makes Cyclic peptides more advantageous when crossing the hydrophobic regions of the cell membrane. In contrast, while linear peptides can also form intramolecular hydrogen bonds, their flexible conformation makes these hydrogen bonds more difficult to form thermodynamically.
Cyclic peptides can be classified according to their size, ring formation method, number of main-chain rings, and bonding method:
Cyclic peptide Ring Formation Methods
Cyclic peptides, as a unique form of drug molecules, offer significant advantages in drug development over small molecules, linear peptides, and antibodies. Compared to small molecules, Cyclic peptides can target flat or shallow protein interfaces that are typically difficult for small molecules to bind to, filling the "drugability gap" between small molecules and antibodies. The advantages of Cyclic peptides over linear peptides have been detailed earlier, and will not be repeated here. When compared to antibodies, Cyclic peptides have a lower molecular weight, enabling them to penetrate solid tumor tissues or the blood-brain barrier and target intracellular sites. Despite these advantages, Cyclic peptides also have some limitations. First, although their permeability is improved over that of linear peptides, the presence of amide bonds and polar side chains in their cyclic structure may still limit their overall permeability. Second, the synthesis of Cyclic peptides requires precise cyclization reactions (such as amide bond formation or disulfide bond cyclization), and the efficiency of cyclization is influenced by factors such as sequence length and steric hindrance from amino acid side chains, resulting in low yields. Additionally, although Cyclic peptides exhibit some resistance to proteolytic hydrolysis, they are still rapidly cleared by the liver and kidneys in vivo, which limits the duration of their therapeutic effects.
| Properties | Small Molecules | Linear Peptides | Cyclic peptides | Antibodies |
|---|---|---|---|---|
| Molecular Weight | < 500 Da | 500 ~ 10,000 Da | > 10,000 Da | |
| Stability | High | Relatively high | High | Low |
| Affinity | Low | Relatively high | Relatively high | High |
| Specificity | Weak | Strong | Strong | Strong |
| Permeability | High | Low | Relatively high | Low |
| Immunogenicity | None | Low | Low | High |
Number and Types of Peptide Drugs on the Market
As of 2024, global regulatory agencies have approved 120 peptide drugs, including both therapeutic and diagnostic agents. Cyclic peptide drugs account for 46% of this total. The potential of peptides in disease treatment was first demonstrated in 1920 when insulin was used for the treatment of diabetes. In 1942, the first Cyclic peptide, Gramicidin S, was discovered. This Cyclic peptide antibiotic, composed of 10 amino acids, is produced by Bacillus brevis and has broad-spectrum antibacterial activity. It was also the first Cyclic peptide to be used as a drug. In 1948, the U.S. Food and Drug Administration (FDA) approved Bacitracin, the first Cyclic peptide drug. Bacitracin, originally isolated from Bacillus subtilis and Bacillus licheniformis, has broad-spectrum antibacterial activity and is primarily used for treating and preventing skin infections. In 1983, the FDA approved Cyclosporine A, the first oral Cyclic peptide drug. Cyclosporine A, a calcium-dependent phosphatase inhibitor derived from the fungus Tolypocladium inflatum, is used to treat autoimmune diseases and prevent organ transplant rejection.
Since the approval of Cyclosporine A by the FDA in 1983, significant progress has been made in the field of Cyclic peptide drugs. However, the number of Cyclic peptide drugs that can be absorbed orally remains limited, with the vast majority of Cyclic peptides requiring injection. This limitation primarily arises from the nature of peptide drugs and the physiological environment of the gastrointestinal tract. First, peptide drugs have larger molecular weights, higher polarity, and complex structures, which make it difficult for them to passively diffuse through the gastrointestinal mucosa into the bloodstream. Second, proteases in the gastrointestinal tract quickly degrade peptides, reducing their bioavailability. Additionally, the absorption of peptide drugs in the gastrointestinal tract is influenced by various factors, such as drug release rate, intestinal permeability, and first-pass liver metabolism. Despite these challenges, recent advancements, including molecular structure modifications, the addition of absorption enhancers, and optimization of drug carriers, have improved the feasibility of oral peptide drug administration. In the future, with the continuous development of new technologies, the development of oral peptide drugs is expected to achieve further breakthroughs.
| Name | Number of Amino Acids | Bond to Form Cycle | MW | Administration | Oral Bioavailability | Design | Year |
|---|---|---|---|---|---|---|---|
| Bacitracin | 11 | head-to-side chain (amide Lys-Asn) | 1422.71 | IM, TOPICAL, OPHTALMIC, ORAL | BLQ | Natural | 1948 |
| Polymyxin B | 11 | head-to-side chain (Thr-Dab) | 1203.49 | IM, IV, IT, OPHTALMIC, ORAL, TOPICAL | BLQ | Natural | 1951 |
| Vancomycin | N.A. | C-C; C-O | 1449.27 | IV, ORAL | < 10% | Natural | 1958 |
| Desmopressin | 9 | side chain-to-side chain (disulfide) | 1069.22 | NASAL, ORAL, SL, IM, SC | 0.08-0.16% | Analogue | 1978 |
| Cyclosporine | 11 | head-to-tail | 1202.635 | TOPICAL, OPHTALMIC, IV, ORAL | 30% | Natural | 1983 |
| Teicoplanin | N.A. | C-C-O; C-C-C | 1879.67 | IM, ORAL | BLQ | Natural | 1987 |
| Octreotide | 8 | side chain-to-side chain (disulfide) | 1019.25 | SC, IV,IM, ORAL | 0.5-0.7% | Analogue | 1988 |
| Linezolid | 14 | 3 side chain-to-side chain (disulfide) | 1526.73 | ORAL | 0.10% | Analogue | 2012 |
| Paritaprevir | 6 | alkene | 765.89 | ORAL | 27% | Heterologous | 2014 |
| Grazoprevir | 5 | side chain-to-tail | 766.91 | ORAL | 27% | Heterologous | 2016 |
| Plectanavir | 16 | 2 side chain-to-side chain (disulfide) | 1681.89 | ORAL | BLQ | Analogue | 2017 |
| Voxilaprevir | 5 | side chain-to-tail | 868.94 | ORAL | / | Heterologous | 2017 |
| Vosolcoprevir | 11 | head-to-tail | 1214.646 | ORAL | 50% | Analogue | 2021 |
Currently, the common cyclic peptide drug screening methods mainly include Phage Display, mRNA Display, DEL (DNA-encoded library), Structure Guided Design, etc. The schematic diagram and advantages and disadvantages are summarized as follows.
| Advantages | Disadvantages |
|---|---|
| 1. High throughput: Can construct libraries containing approximately 10^8 peptides. By linking with target proteins, it can select peptides with high binding affinity, leading to more diverse results. 2.Relatively Simple Operation: Based on phage display technology, the process leverages the unique properties of phages to display and select peptides through steps such as bacterial infection, screening, and amplification. This technology is highly mature and well-established. 3.Wide Applicability: This method can be used to screen high-affinity binders for a variety of protein and nucleic acid targets, including some targets that are difficult to screen using traditional methods. | 1. Limited library diversity: Although it can construct relatively large peptide libraries, the mRNA Display technology and some peptide libraries have limitations in diversity, with the possibility of fewer candidates that can bind to the target. 2. Selection cycle time: Requires several cycles of selection, amplification, and screening, making the overall process time-consuming. 3. Limited Screening of Intracellular Targets: Since peptides displayed by phages are synthesized within bacterial cells, it is typically challenging to identify peptides with cell membrane penetration ability. This creates certain limitations in drug development targeting intracellular sites. |
| Advantages | Disadvantages |
|---|---|
| 1. Larger library diversity: Can construct libraries reaching 10^11 to 10^15 in size, which increases the possibility of selecting a wide variety of peptides with high affinity and binding potential. 2. No cell-based expression: Peptides are expressed through external translation and purification systems, which bypasses the limitations of cellular expression systems and allows for better peptide selection. 3. Integrable genetic code reprogramming: Ability to introduce a variety of unnatural amino acids, further expand the chemical space for screening, and increase the possibility of screening peptides with specific functions or pharmacological activities. | 1. Technology complexity: Requires more complex external systems for translation and selection, making it difficult to implement and requiring advanced technology support. 2. Selection cycle time: Due to the need for large-scale libraries, it involves multiple rounds of screening, leading to long selection cycles. 3. Screening of peptide drugs for intracellular targets is limited: Although it is possible to introduce optimized peptides such as non-natural amino acids, there are still certain technical challenges in screening out peptides that can effectively penetrate the cell membrane and act on intracellular targets. |
| Advantages | Disadvantages |
|---|---|
| 1. Library diversity: Can use nucleotide building blocks to include both natural and non-natural peptides or other functional groups. 2. Fast recognition and screening: DNA labeling allows for fast PCR amplification and screening of target sequences without requiring the chemical synthesis of peptide libraries. | 1. Difficulty in combining with strong ligands: It is difficult to evaluate the activity of peptides in real biological environments after synthesis. 2. Synthetic limitations: The chemical synthesis of cyclic peptides might not match DNA labeling efficiency, leading to discrepancies in DNA-labeling results. |
| Advantages | Disadvantages |
|---|---|
| 1. Precise design: Based on known molecular fragments and peptide structures, it can design peptides with high specificity and diversity targeting desired sequences. 2. Fewer selection steps: By using structural information, fewer steps are required to select peptides, enabling faster identification of peptides with optimal binding affinity. 3. Optimizable function: Can improve the functionality of the peptides based on their designed structure, enhancing the precision of their biological function. | 1. Requires high-quality structural information: Requires high-quality structural information, such as detailed 3D structures like those from X-ray crystallography or cryo-EM, for accurate design. 2. Design complexity: Requires in-depth design of the structure of the molecule and its interaction with the target, which may require high-level expertise in structural biology. 3. Limited applicability to complex targets: For some targets with complex structures and large dynamic changes, it may be difficult to accurately design ideal peptides. |
From the above screening methods, it can be seen that cyclic peptide screening is mainly composed of three parts: encoding, decoding and peptide methodology. The encoding part can use biological systems, such as DNA, or chemical means, such as peptides. Correspondingly, decoding corresponds to DNA sequencing or mass spectrometry. Among them, peptide methodology is the core link of screening. In biological systems, it is mainly achieved through enzymatic reactions; in chemical systems, the applicable means are more abundant, including highly specific and highly reactive amino acid organic reactions. Therefore, the cyclic peptide library constructed by chemical methods (DEL) has more advantages in structural diversity and its types are also the richest.
| Encoding Section | Biological Systems: For example, DNA can serve as an encoding system. DNA-encoded libraries (DEL) use DNA tags to label compounds, which are then decoded using high-throughput sequencing technology. This process enables rapid identification of compounds that bind to the target. |
| Chemical Methods: For instance, peptides can be constructed through chemical synthesis to create Cyclic peptide libraries. The construction of these libraries can be achieved through various strategies, such as using natural amino acids or introducing non-natural amino acids to increase diversity. | |
| Decoding Section | DNA Sequencing: In DNA-based encoding systems, the decoding section involves DNA sequencing. High-throughput sequencing technologies can quickly and accurately read DNA tags, allowing the identification of compounds that exhibit binding activity with the target. |
| Mass Spectrometry: In peptide-based encoding systems, mass spectrometry is commonly used for decoding. Mass spectrometry can precisely measure the molecular weight of peptides, enabling the identification of specific peptide sequences. | |
| Peptide Methodology Section | Enzymatic Reactions: In biological systems, enzymatic reactions are used for peptide synthesis and modification. For example, specific enzymes can be employed to carry out peptide cyclization reactions, which enhance the stability and specificity of peptides. |
| Chemical Reactions: In chemical systems, highly reactive amino acids and organic reactions are widely applied. For example, thiol-disulfide exchange reactions and reversible Michael acceptor-thiol addition reactions can be used for the efficient modification and assembly of peptides. |
In the past decade, combinatorial libraries that integrate multiple encoding and display technologies have become a highly valuable and versatile alternative to traditional high-throughput screening (HTS) in the field of drug discovery. These combinatorial libraries combine affinity-based phenotypic screening and genotypic screening, yet their principles and approaches are fundamentally different. Display technologies typically rely on protein fusion to screen for target affinity, while encoding libraries connect target proteins with compounds used for similar purposes via chemical synthesis. The former emphasizes biochemical activity, while the latter faces challenges in library construction. Display technologies are closely tied to the central dogma of genetics, where transcription and translation provide inherent advantages for target discovery. However, this also means the library's size and diversity are constrained by natural amino acids, inherently limiting their diversity. Encoding technologies rely on synthetic compound libraries, where purity and content present significant challenges for screening. Whether using display technologies or encoding libraries, these methods can only assess a molecule's binding ability but cannot directly conduct functional assays during the screening process. As a result, they struggle to accurately evaluate a molecule's functional activity in real physiological environments, such as reporter gene activity and cellular activity. This limitation can lead to false positives, which can reduce the accuracy and reliability of the screening.
With the continuous development of novel encoding technologies, screening methods have expanded from traditional solution-phase and immobilized proteins to living cell environments. This shift aims to better reflect the protein activity state under physiological conditions, significantly improving screening success rates. In recent years, high-throughput synthetic screening platforms have made significant progress in the development of Cyclic peptide drugs. With the rapid development of artificial intelligence (AI) technology, its application in drug development is expanding, and its deep integration with novel screening technologies provides new opportunities to accelerate the drug discovery process. By using AI to screen and enrich vast chemical spaces, combined with high-throughput screening techniques, AI will rapidly and precisely identify active molecules for various targets, greatly enhancing screening efficiency and accuracy while significantly shortening the research and development cycle. At the same time, the accumulation of data and the iterative interaction of AI models will create a powerful feedback loop, continuously driving innovation and breakthroughs in drug development.
| CAT# | Product Name | M.W | Molecular Formula | Get a Quote |
|---|---|---|---|---|
| 10-101-103 | Vancomycin | 1449.25 | C66H75Cl2N9O24 | Inquiry |
| 10-101-104 | Teicoplanin | 1879.66 | C88H95Cl2N9O33 | Inquiry |
| 10-101-112 | Bremelanotide | 1025.2 | C50H68N14O10 | Inquiry |
| 10-101-169 | Pasireotide | 1047.2 | C58H66N10O9 | Inquiry |
| 10-101-186 | Romidepsin | 540.69584 | C24H36N4O6S2 | Inquiry |
| 10-101-325 | Semaglutide | 4113.57 | C187H291N45O59 | Inquiry |
| 10-101-62 | Ziconotide | 2639.2 | C102H172N36O32S7 | Inquiry |
| 10-101-78 | Dalbavancin | 1816.69 | C88H100Cl2N10O28 | Inquiry |
| AF083 | Polymyxin B | Inquiry | ||
| MFP-041 | Rezafungin | 1226.4 | C63H85N8O17 | Inquiry |
| R04030 | Cyclo(-Arg-Gly-Asp-D-Phe-Val) | 574.6 | C26H38N8O7 | Inquiry |
| R1574 | Octreotide | 1019.2 | C49H66N10O10S2 | Inquiry |
| R1812 | Lanreotide | 1096.3 | C54H69N11O10S2 | Inquiry |
| R1824 | Cyclo(RGDyK) | 847.7 | C31H43F6N9O12 | Inquiry |
| R2018 | Capreomycin | Inquiry | ||
| R2029 | Enviomycin | 685.69 | C26H43N11O11 | Inquiry |
| R2052 | Zilucoplan | 3562 | C172H278N24O55 | Inquiry |
| R2238 | Telavancin | 1755.6 | C80H106Cl2N11O27P | Inquiry |
| R2239 | Oritavancin | 1793.1 | C86H97Cl3N10O26 | Inquiry |
| R2240 | Bacitracin | 1422.69 | C66H103N17O16S | Inquiry |
| R2241 | Viomycin | 685.69 | C25H43N13O10 | Inquiry |
| R2242 | Colistin | 1169.5 | C53H100N16O13 (for E1) | Inquiry |
| R2243 | Micafungin | 1270.27 | C56H71N9O23S | Inquiry |
| R2244 | Anidulafungin | 1140.24 | C58H73N7O17 | Inquiry |
| R2245 | Fostamatinib | 580.46 | C23H26FN6O9P | Inquiry |
| R2246 | Paritaprevir | 765.88 | C40H43N7O7S | Inquiry |
| R2247 | Grazoprevir | 766.9 | C38H50N6O9S | Inquiry |
| Z10-101-154 | Daptomycin | 1620.69 | C72H101N17O26 | Inquiry |
| Z10-101-157 | Caspofungin | 1093.31 | C52H88N10O15 | Inquiry |