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The diverse physiological and pathophysiological roles of peptide–protein and protein–protein interactions provide potential opportunities for therapeutic intervention in a wide range of diseases. In retrospect, peptide drug discovery has faced substantial challenges in achieving its full potential towards modulating such biological processes, due to inherent limitations in overcoming susceptibility to proteolytic degradation, cell penetration and achieving suitable in vivo pharmacokinetic profiles. Unsurprisingly, in part owing to such challenges, peptide drug discovery has historically been restricted to extracellular and receptor target space (e.g., G-protein coupled receptor agonists or antagonists). This Chapter highlights a renaissance of peptide drug discovery relative to drug design, chemical space, cell permeability and drug delivery to tackle an expanding target space that is no longer restricted to only extracellular and receptor targets. Noteworthy are past milestones in peptide science and technology, including the first and second waves of marketed peptide drugs, as well as the current focus on exploiting the peptide modality to tackle “undruggable” target space (relative to small molecules or antibodies) such as intracellular protein–protein interactions. Unquestionably, a worldwide network of academic and biotech/pharma drug hunters has grown significantly to date and is driving the third wave of peptide drug discovery. For those of us who have been deeply engaged in the quest for peptide breakthrough medicines and disruptive innovation over many years there may be a sense of “there and back again”; however, there is a new mounting enthusiasm to re-engage the peptide modality for providing hope to many who suffer with life-threatening and debilitating disease.

Peptide drug discovery has evolved from highly focused efforts on specific receptors and proteases to a plethora of targets spanning receptors to enzymes and protein–protein interactions, and shattering a long-lived challenge to penetrate into cells to modulate intracellular targets in promising ways to expand “druggable” target space. Since the turn of the new millennium (2000) there is no doubt among peptide scientists that there is a genuine renaissance of peptide drug discovery. So, what has really inspired and propelled such a renaissance? Scholarly passion and intellectual perseverance have been absolutely essential, as this has been the proverbial long and winding road. And, of course, science and technology are empowering key advancements. In particular, synthetic chemistry (e.g., novel amino acid building blocks and peptide secondary structure mimetics) and super-diverse phage-display, mRNA-display and DNA-encoded libraries are expanding peptide chemical space in amazing ways. Likewise, molecular genetics, structural biology and computational chemistry are continuing to play powerful roles to unveil extraordinary target space opportunities. Furthermore, our increased understanding of the complex pharmacology of disease mechanisms and peptide drug delivery, including cell permeability to prosecute intracellular targets, is re-defining the term “druggability”. Beyond such convergent, multidisciplinary science and enabling technology, the steady growth of a pipeline of marketed and clinically investigated peptides, as well as a competitive resurgence of peptide research and development in both pharma and biotech are propelling this renaissance of the peptide therapeutic modality.

Historically, one of the earliest archetypal flags in the ground for peptide chemistry can be traced to that of Emil Fischer and the synthesis of the simplest dipeptide Gly-Gly at the very beginning of the twentieth century. In retrospect the sheer number of milestones that deserve recognition are well beyond those highlighted in this chapter (Figure 1.1), albeit they exemplify some quite significant achievements in peptide science and technology. Specifically, these include the discoveries of insulin, penicillin, oxytocin, pepstatin, thyrotropin-releasing hormone, gonadotropin-releasing hormone (GnRH), somatostatin, melanocyte-stimulating hormone (MSH), enkephalin and, more recently, tri-cystine knotted cyclotides. They include the development of potent peptide and peptidomimetic analogs thereof that have provided working models for bioactive conformations, agonist/antagonist pharmacophores and cellular receptor signaling mechanisms. Likewise, and with respect to intracellular targets, the natural product macrocyclic peptide cyclosporine A, HIV Tat (a progenitor of the first cell-penetrating peptides), synthetic peptidomimetic HIV protease inhibitors, designed non-peptide Src homology-2 antagonists and macrocyclic α-helical proteomimetic antagonists of MDM2/X collectively illustrate the scope of both past and current peptide drug discovery approaches to overcome the challenge of cell permeability. Lastly, some key past and emerging disruptive innovations that have leveraged the power of molecular biophysics and molecular diversity to enable peptide drug discovery include X-ray crystallography (i.e., identification of canonical secondary structures such as the α-helix and β-sheet), solid-phase peptide synthesis, synthetic peptide/peptoid libraries, phage-display peptide (monocyclic/bicyclic) libraries and mRNA-displayed macrocyclic peptide libraries. In the case of macrocyclic peptides, there is no doubt that the impact of stapled helical peptides and non-helical macrocyclic peptides having varying size and inclusion of N-methyl amino acids, d-amino acids and other unique amino acid building blocks is both driving the generation of novel lead molecules and expanding druggable target space. Lastly, varying chemical modification of clinically investigated peptides that exemplify improvement in pharmacokinetic (half-life) properties have been achieved (see later), and include lipidation, pegylation and, more recently, macrocyclization (e.g., amphipathic α-helical stapled peptides).

Figure 1.1

Some key milestones in peptide science and technology. Several “waves” of peptide and peptidomimetic drug discovery over the past three decades are highlighted.

Figure 1.1

Some key milestones in peptide science and technology. Several “waves” of peptide and peptidomimetic drug discovery over the past three decades are highlighted.

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Since the 1980s, several hundred peptide and peptidomimetic candidates have advanced into clinical trials for a wide range of therapeutic indications,1–3  including endocrine, metabolic, cardiovascular, cancer, immune and central nervous system diseases, and more than 50 such agents have been approved by the United States Food and Drug Administration (Table 1.1). Several of these drugs have achieved major commercial success, including Lupron, Zoladex, Sandostatin, Byetta and Forteo. Unquestionably, a strong understanding of the structure–activity relationships of such peptides at their specific targets and translation to in vivo preclinical disease models has been critical for their drug development. Likewise, overcoming challenges such as the metabolic instability (owing to rapid degradation by proteolytic enzymes) and generally poor pharmacokinetic properties of peptides has been strategic for their optimization relative to in vivo efficacy and route of administration.

Table 1.1

Some peptide and peptidomimetic drugs approved by the United States Food and Drug Administration (FDA) or European Medicines Agency (EMA)

Trade name (INN)Therapeutic targetPrimary useApproval
DDAVP (desmopressin) Vasopressin receptora Diabetes insipidus 1978 (FDA) 
Sandimmune (cyclosporine) Cyclophilin/calcineurinb Immunotherapy 1983 (FDA) 
Lupron (leuprorelin) GnRH receptora Oncology 1985 (FDA) 
Zoladex (goserelin) GnRH receptora Oncology 1989 (FDA) 
Invirase (saquinavir) HIV-1 proteaseb Infectious disease 1996 (FDA) 
Copaxone (glatiramer) T-cell functiona Allergy, immunology 1996 (FDA) 
Crixivan (indinavir) HIV-1 proteaseb Infectious disease 1996 (FDA) 
Viracept (nelfinavir) HIV-1 proteaseb Infectious disease 1997 (FDA) 
GlucaGen (recombinant glucagon) Glucagon receptora Metabolic 1998 (FDA) 
Integrilin (eptifibatide) Integrin receptora Cardiovascular 1998 (FDA) 
Sandostatin (octreotide) Somatostatin receptora Acromegly 1998 (FDA) 
Angiomax (bivalirudin) Thrombina Hematology 2000 (FDA) 
Agenerase (amprenavir) HIV-1 proteaseb Infectious disease 1999 (FDA) 
Cetrotide (cetrorelix) GnRH receptora Endocrinology 2000 (FDA) 
Trelstar (triptorelin) GnRH receptora Oncology 2000 (FDA) 
Natrecor (nesiritide) Natriuretic peptide receptora Cardiovascular 2001 (FDA) 
Byetta (exenatide) Glucagon-like peptide-1 receptora Metabolic 2002 (FDA) 
Forteo (teriparatide) Parathyroid hormone receptora Metabolic 2002 (FDA) 
Neulasta (pegfilgrastim) G-CSF receptora Oncology 2002 (FDA) 
Reyataz (atazanavir) HIV-1 proteaseb Infectious disease 2003 (FDA) 
Cubicin (daptomycin) Bacterial cell membranec Antibacterial 2003 (FDA) 
Fuzeon (enfuvirtide) gp41 of HIV fusion complexa Infectious disease 2003 (FDA) 
Plenaxis (abarelix) GnRH receptora Oncology 2003 (FDA) 
Velcade (bortezomib) 26S proteasomeb Oncology 2003 (FDA) 
Prialt (ziconotide) N-type calcium channela Central nervous system 2004 (FDA) 
Symlin (pramlintide) Amylin receptora Metabolic 2005 (FDA) 
Vantas (histrelin) GnRH receptora Oncology 2005 (FDA) 
Prezista (darunavir) HIV-1 proteaseb Infectious disease 2006 (FDA) 
Somatuline (lanreotide) Somatostatin receptora Endocrinology 2007 (FDA) 
Firmagon (degarelix) GnRH receptora Oncology 2009 (FDA) 
Victoza (liraglutide) Glucagon-like peptide-1 receptora Metabolic 2010 (FDA) 
Linzess (linaclotide) Guanylate cyclase receptora Gastrointestinal 2012 (FDA) 
Signifor (pasireotide) Somatostatin receptora Cushing’s disease 2012 (FDA) 
Gattex (teduglutide) Glucagon-like peptide-2 receptora Gastrointestinal 2012 (FDA) 
Kyprolis (carfilzomib) Proteasomeb Oncology 2012 (FDA) 
Scenesse (afamelanotide) Melanocortin-1 receptora Skin pigmentation 2014 (EMA) 
Afrezza (inhaled insulin) Insulin receptora Diabetes 2014 (FDA) 
Saxenda (liraglutide) Glucagon-like peptide-1 receptora Metabolic 2014 (FDA) 
Trulicity (dulaglutide) Glucagon-like peptide-1 receptora Metabolic 2014 (FDA) 
Ninlaro (ixazomib) Proteasomeb Oncology 2015 (FDA) 
Pabal (carbetocin) Oxytocin receptora Obstetrics 2015 (EMA) 
Natpara (parathyroid hormone) Parathyroid hormone receptora Hypocalcemia 2015 (FDA) 
Toujeo (insulin glargine) Insulin receptora Diabetes 2015 (FDA) 
Tresiba (insulin degludec) Insulin receptora Diabetes 2015 (FDA) 
Adlyxin (lixisenatide) Glucagon-like peptide-1 receptora Metabolic 2016 (FDA) 
Zepatier (grazoprevir) HCV proteaseb Infectious disease 2016 (FDA) 
Trade name (INN)Therapeutic targetPrimary useApproval
DDAVP (desmopressin) Vasopressin receptora Diabetes insipidus 1978 (FDA) 
Sandimmune (cyclosporine) Cyclophilin/calcineurinb Immunotherapy 1983 (FDA) 
Lupron (leuprorelin) GnRH receptora Oncology 1985 (FDA) 
Zoladex (goserelin) GnRH receptora Oncology 1989 (FDA) 
Invirase (saquinavir) HIV-1 proteaseb Infectious disease 1996 (FDA) 
Copaxone (glatiramer) T-cell functiona Allergy, immunology 1996 (FDA) 
Crixivan (indinavir) HIV-1 proteaseb Infectious disease 1996 (FDA) 
Viracept (nelfinavir) HIV-1 proteaseb Infectious disease 1997 (FDA) 
GlucaGen (recombinant glucagon) Glucagon receptora Metabolic 1998 (FDA) 
Integrilin (eptifibatide) Integrin receptora Cardiovascular 1998 (FDA) 
Sandostatin (octreotide) Somatostatin receptora Acromegly 1998 (FDA) 
Angiomax (bivalirudin) Thrombina Hematology 2000 (FDA) 
Agenerase (amprenavir) HIV-1 proteaseb Infectious disease 1999 (FDA) 
Cetrotide (cetrorelix) GnRH receptora Endocrinology 2000 (FDA) 
Trelstar (triptorelin) GnRH receptora Oncology 2000 (FDA) 
Natrecor (nesiritide) Natriuretic peptide receptora Cardiovascular 2001 (FDA) 
Byetta (exenatide) Glucagon-like peptide-1 receptora Metabolic 2002 (FDA) 
Forteo (teriparatide) Parathyroid hormone receptora Metabolic 2002 (FDA) 
Neulasta (pegfilgrastim) G-CSF receptora Oncology 2002 (FDA) 
Reyataz (atazanavir) HIV-1 proteaseb Infectious disease 2003 (FDA) 
Cubicin (daptomycin) Bacterial cell membranec Antibacterial 2003 (FDA) 
Fuzeon (enfuvirtide) gp41 of HIV fusion complexa Infectious disease 2003 (FDA) 
Plenaxis (abarelix) GnRH receptora Oncology 2003 (FDA) 
Velcade (bortezomib) 26S proteasomeb Oncology 2003 (FDA) 
Prialt (ziconotide) N-type calcium channela Central nervous system 2004 (FDA) 
Symlin (pramlintide) Amylin receptora Metabolic 2005 (FDA) 
Vantas (histrelin) GnRH receptora Oncology 2005 (FDA) 
Prezista (darunavir) HIV-1 proteaseb Infectious disease 2006 (FDA) 
Somatuline (lanreotide) Somatostatin receptora Endocrinology 2007 (FDA) 
Firmagon (degarelix) GnRH receptora Oncology 2009 (FDA) 
Victoza (liraglutide) Glucagon-like peptide-1 receptora Metabolic 2010 (FDA) 
Linzess (linaclotide) Guanylate cyclase receptora Gastrointestinal 2012 (FDA) 
Signifor (pasireotide) Somatostatin receptora Cushing’s disease 2012 (FDA) 
Gattex (teduglutide) Glucagon-like peptide-2 receptora Gastrointestinal 2012 (FDA) 
Kyprolis (carfilzomib) Proteasomeb Oncology 2012 (FDA) 
Scenesse (afamelanotide) Melanocortin-1 receptora Skin pigmentation 2014 (EMA) 
Afrezza (inhaled insulin) Insulin receptora Diabetes 2014 (FDA) 
Saxenda (liraglutide) Glucagon-like peptide-1 receptora Metabolic 2014 (FDA) 
Trulicity (dulaglutide) Glucagon-like peptide-1 receptora Metabolic 2014 (FDA) 
Ninlaro (ixazomib) Proteasomeb Oncology 2015 (FDA) 
Pabal (carbetocin) Oxytocin receptora Obstetrics 2015 (EMA) 
Natpara (parathyroid hormone) Parathyroid hormone receptora Hypocalcemia 2015 (FDA) 
Toujeo (insulin glargine) Insulin receptora Diabetes 2015 (FDA) 
Tresiba (insulin degludec) Insulin receptora Diabetes 2015 (FDA) 
Adlyxin (lixisenatide) Glucagon-like peptide-1 receptora Metabolic 2016 (FDA) 
Zepatier (grazoprevir) HCV proteaseb Infectious disease 2016 (FDA) 
a

Extracellular/receptor therapeutic targets.

b

intracellular therapeutic targets.

c

antibiotic peptides that disruptive bacterial membranes.

Peptide oral bioavailability remains elusive, although what was once thought to be the exceptional case of cyclosporine A is changing as a result of a deeper analysis of macrocyclic peptides to understand the relationship of the structural and conformational impact of backbone modifications, ring size and side-chain lipophilicity to passive transport (see later). Consequently, a majority of marketed peptide therapeutics leverage subcutaneous and injectable routes of administration. Such modified peptides and peptidomimetics exemplify classic hierarchical strategies4–20  to achieve an effective combination of high affinity to target and proteolytic stability, including (i) backbone amide N-alkylation; (ii) backbone amide replacement with non-hydrolyzable surrogates; (iii) amino acid Cα-stereoinversion and/or Cα-alkylation; (iv) β-amino acids; (v) cyclic α-/β-amino acids; (vi) dipeptide replacements that mimic canonical secondary structural motifs such as α-helix, β-strand/β-sheet or β-/γ-turns; and (vii) macrocyclization designed to stabilize α-helical, β-strand/β-sheet, β-/γ-turns and/or other conformationally restricted peptide/peptidomimetic chemotypes (e.g., monocyclic or multicyclic). A few examples of the pioneering and contemporary chemistry that has contributed to modified peptides and peptidomimetics are described below.

For receptor-targeted peptide therapeutics, long-lasting exposure levels in vivo have been achieved using varying approaches,21–27  including both chemical conjugation (e.g., fatty acids, polyethylene glycol, antibodies and related recombinant proteins and serum albumin) and sustained-release formulations applicable to parenteral routes of administration (e.g., subcutaneous). Specific examples include the palmitoyl-modified glucagon-like peptide-1 (GLP-1) agonist liraglutide,28  a pegylated GLP-1 agonist/glucagon antagonist hybrid peptide,29  the pegylated granulocyte colony-stimulating factor drug pegfilgrastim30  and a human Fc domain–thrombopoietin peptide agonist conjugate romiplostim.31  Although absolute exposure levels, in terms of both time and concentration, are case-specific, the relatively short half-lives (typically a few minutes) of most endogenous (native) peptides provide an opportunity to advance viable drugs with substantially improved metabolic and pharmacokinetic properties.

Indubitably, the greatest impact of peptide drug discovery so far has been that focused on receptor target space, especially the G protein-coupled receptor (GPCR) group (e.g., class A and B GPCRs), which has been determined using human genome sequencing to be one of the largest protein families32–34  (Figure 1.2), and this has translated to the first wave of peptide therapeutics.9,11,14,19,35  Pioneering studies on class A GPCR peptides may be traced to oxytocin, vasopressin, α-MSH, GnRH, somatotropin-release inhibiting factor (somatostatin) and the opioid peptides (e.g., enkephalin, β-endorphin, dynorphin) during the 1970s–1990s. Likewise, but more recently, a second wave of peptide drug discovery has successfully extended to class B GPCRs as exemplified by glucagon-like peptide-1 (GLP)-1, islet amyloid polypeptide (amylin), GLP-2 and parathyroid hormone.

Figure 1.2

Human genome mapping of the G protein-coupled receptor (GPCR) superfamily.

Figure 1.2

Human genome mapping of the G protein-coupled receptor (GPCR) superfamily.

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Importantly, these early class A and B GPCR peptide agonist/antagonist structure–activity studies provided insight to understand the intrinsic peptide conformational properties as well as predictive 3D-pharmacophore models.14,19,35  Unfortunately, such pioneering studies were not empowered by high-resolution X-ray crystallographic structures of class A and B GPCRs until more recently.36,37  Instead, a systematic analysis of peptide structure–activity relationships (e.g., analog modifications by d-amino acids, N-alkyl-amino acids, Cα-alkyl-amino acids and/or macrocyclization) as well as biophysical characterization (e.g., nuclear magnetic resonance (NMR) spectroscopy and circular dichroism) revealed that β-turn and α-helix secondary structures often correlated with GPCR molecular recognition for such peptides (Figure 1.3). As exemplified by Scenesse, Lupron and Sandostatin, incorporation of a d-amino acid regiospecificially within their peptide sequences has been conceptualized to stabilize β-turn conformations of their respective class A GPCR agonist pharmacophores.38–40  In contrast, Byetta, Forteo and Gattex share relatively high propensities for α-helix conformations that have been correlated with their molecular recognition of and binding to and activation of their respective class B GPCRs.41–43 

Figure 1.3

Amino acid sequences of several known G protein-coupled receptor (GPCR)-targeted marketed peptide drugs that further show β-turn and α-helix substructural motifs proposed for their bioactive conformations.

Figure 1.3

Amino acid sequences of several known G protein-coupled receptor (GPCR)-targeted marketed peptide drugs that further show β-turn and α-helix substructural motifs proposed for their bioactive conformations.

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As a personal reflection on the first wave of peptide drug discovery, my first scientific foray at the University of Arizona contributed to the discovery of class A GPCR superagonist peptides for the melanocortin receptor MC1R38,44–46 , namely Scenesse™ (Figure 1.3) and cyclo[Cys4, Cys10]α-MSH (Figure 1.4). Indisputably, these two molecules have inspired the design of numerous linear and macrocyclic α-MSH peptide analogs,47,48  including the recent clinical development macrocyclic α-MSH peptide analog setmelanotide (Figure 1.4) from Rhythm Pharmaceuticals.49  It is noteworthy that the d-Phe7 modification and macrocyclization about the central pharmacophore tetrapeptide within these peptide agonists has been consistent with stabilizing a predicted β-turn (Figure 1.4). Furthermore, the second-generation macrocyclic α-MSH superagonist Ac-cyclo[Nle4, Asp5, d-Phe7, Lys10]α-MSH4-10-NH246  and the structurally related α MSH antagonist analog Ac-cyclo[Nle4, Asp5, d-Nal(2′)7, Lys10]α-MSH4-10-NH250  (Figure 1.4) from the University of Arizona exemplify significant benchmarks to enable the design of MSH ligands for the MC1R, MC3R, MC4R and MC5R. Most recently, MCR relationships to several key diseases (e.g., energy homeostasis, inflammation and neurodegeneration) have been described51–53  and implicate new opportunities to leverage MCR-specific peptide and non-peptide agonists/antagonists.47,48,54 

Figure 1.4

Structures of [Cys4,10]α-MSH, Ac-cyclo[Nle4, Asp5, d-Phe7, Lys10]α-MSH4-10-NH2, setmelanotide and cyclo[Nle4, Asp5, d-Nal(2′)7, Lys10]α-MSH4-10-NH2 highlighting the β-turn conformations about the central His-Phe-Arg-Trp sequence of MSH.

Figure 1.4

Structures of [Cys4,10]α-MSH, Ac-cyclo[Nle4, Asp5, d-Phe7, Lys10]α-MSH4-10-NH2, setmelanotide and cyclo[Nle4, Asp5, d-Nal(2′)7, Lys10]α-MSH4-10-NH2 highlighting the β-turn conformations about the central His-Phe-Arg-Trp sequence of MSH.

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The development of peptide agonists for the GLP-1 receptor has been advanced in what may be the most competitive worldwide efforts focused on class B GPCRs. Such efforts have been focused on type 2 diabetes mellitus and obesity, including glucose homeostasis and regulation of gastric motility and food intake.55–57  Currently, several GLP-1 receptor-targeted peptide agonists have reached the market and representative examples include exenatide, liraglutide, lixisenatide and semaglutide (Figure 1.5). Such GLP-1 peptide agonists illustrate regiospecific amino acid modifications to confer metabolic stability as well as the incorporation of fatty acid or other types of conjugation to enhance their in vivo pharmacokinetic and pharmacological properties.58–60  Of historical significance to class B GPCRs, the first X-ray crystallographic structures of GLP-1 receptor extracellular domain complexes with GLP-1 peptide analogs have provided the 3D molecular maps and insight to their receptor binding mechanism.61  Furthermore, the deeper biological study of GLP-1 peptides (including truncated, modified and chimeric analogs) as well as small-molecule modulators of the GLP-1 receptor activation have provided further understanding of molecular recognition and biased cellular signaling.62  Importantly, biased cellular signaling is being found in an increasing number of GPCRs by both peptide and non-peptide agonists.63 

Figure 1.5

Structures of some marketed or clinically advanced (semaglutide) glucagon-like peptide (GLP)-1 agonists.

Figure 1.5

Structures of some marketed or clinically advanced (semaglutide) glucagon-like peptide (GLP)-1 agonists.

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Beyond GPCRs, there has been steady progress in the development of peptide modulators of growth factor and cytokine receptors, integrins and ion channels to expand the scope of receptor target space for peptide drug discovery. Noteworthy for such receptors are the more structurally complex peptide agonists and antagonists, including those having multiple disulfide bridges such as insulin, linaclotide, ziconotide and ProTx-II, and which exemplify high specificity for the insulin receptor, guanylate cyclase-C, N-type Ca2+ channel and voltage-gated Na1+ channel targets, respectively.64–67 

A significant opportunity for the peptide drug modality is emerging relative to a third wave that is focused on modulating intracellular targets relative to stapled α-helical peptides,68–73  structurally/conformationally diverse macrocyclic peptides inspired by cyclosporine A74–81  and both linear and macrocyclic peptides incorporating cell-penetrating peptide motifs.82–85  Both β-strand and α-helix secondary structures are widely found in at the interfaces of protein–protein interactions, and have been described86–91  with respect to comprehensive analysis of the Protein Data Bank (www.rcsb.org). Examples of β-strand protein–protein interactions include PDZ domains, PTB/PI domains, Mad2:Cdc20, NS3 protease:NS4A and PKA:Rbα.89,90  Representative α-helical protein–protein interactions of >1600 non-redundant, unique high-resolution 3D structures derived from the Protein Data Bank87–91  include BIM BH3:Mcl-1,92  BAD:Bcl-Xl,93  p53:MDM2,94  MAML:Notch,95  HIF-1α:p300,96  Myc-Max,97  eIF4G:eIF4E,98  TIF2:GCCR,99  Scr-1:RORγ100  and IKKβ:NEMO101  (Figure 1.6). As detailed later, α-helical secondary structures have inspired significant peptide drug discovery efforts to modulate such protein–protein interactions, with a specific focus on intracellular targets of therapeutic interest.

Figure 1.6

Some representative protein–protein interactions highlighting α-helical interfaces.

Figure 1.6

Some representative protein–protein interactions highlighting α-helical interfaces.

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Peptide drug discovery leverages the typical high affinity and/or selectivity properties that endogenous peptides have for their cognate targets (e.g., ≤10−9 M range for most GPCR-targeted peptides). We understand that the high-fidelity molecular recognition between peptides and their targets is achieved through intermolecular interactions by means of a dynamic orchestration of specific hydrophobic, electrostatic and hydrogen-bonding forces. Nevertheless, mimicking critical target interactions effectively is a key challenge in peptide drug discovery, owing to the molecular size, conformational flexibility and functional group diversity of the peptides. Our early knowledge of the molecular recognition of peptide hormones with their cognate GPCR targets evolved from studies focused on 3D pharmacophore models of peptide agonists or antagonists generated from structure–activity relationships and conformational analysis from NMR spectroscopy.4–6,9,11,14,19  Our current knowledge is rapidly expanding by way of high-resolution 3D structures of peptide–target complexes, leveraging X-ray crystallography and/or NMR spectroscopy, as well as increasingly sophisticated computational modeling studies.6,7,12,15,36,37,61,86–90,102–117 

Peptide drug discovery has evolved from hierarchical drug design strategies,4–20,68–79,91,118  integrating synthetic chemical modifications of the native peptide or protein, including amino acid substitutions, amide bond replacements, peptide scaffold modification (e.g., peptidomimetic analogs), macrocyclization, secondary structure mimicry and non-peptidic templates (e.g., de novo designed small-molecule replicas of peptide ligands) (Figure 1.7). This peptide drug design and synthetic chemistry has expanded our understanding of β-turns, γ-turns, β-strands, β-sheets, α-helices and 310-helices in terms of both their 3D structural properties and molecular recognition at cognate targets (see later).

Figure 1.7

Hierarchical drug design strategies to advance novel peptide, peptidomimetic and proteomimetic drugs for extracellular/receptor and intracellular targets.

Figure 1.7

Hierarchical drug design strategies to advance novel peptide, peptidomimetic and proteomimetic drugs for extracellular/receptor and intracellular targets.

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The 3D structural and conformational properties of peptides are defined in terms of torsion angles (ψ, ϕ, ω and χ) between the backbone amine nitrogen (Nα), the backbone carbonyl carbon (C′), the backbone methine carbon (Cα) and the side-chain hydrocarbon functionalization (e.g., Cβ, Cγ, Cδ, Cε of Lys) derived from the amino acid sequence (Figure 1.8). A Ramachandran plot (ψ versus ϕ) may be used to analyze the preferred combinations of torsion angles for ordered secondary structures (i.e., conformations) of peptides, such as α-helix, β-turn, γ-turn or β-strand. With respect to the amide bond torsion angle (ω), the trans geometry is more energetically-favored for most natural dipeptide substructures; however, when the C-terminal partner is Pro- or another N-alkylated (including cyclic) amino acid, the cis geometry is probable and may also contribute to β-turn or γ-turn stabilization.

Figure 1.8

3D perspective of peptide structural and conformation properties.

Figure 1.8

3D perspective of peptide structural and conformation properties.

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Molecular flexibility is directly related to covalent and/or non-covalent bonding interactions within a specific peptide. In this regard, a single replacement of hydrogen by a methyl moiety within amino acids (i.e., Nα-methyl, Cα-methyl or Cβ-methyl) may have significant consequences on the local conformational properties of a peptide. The peptide Nα–Cα–C′ and/or Cα–Cβ scaffold may also be transformed to create novel amino acid building blocks4,20,119–128  such as dehydro-amino acids (e.g., Xα–Xβ → X=C), vinylogous amino acids (e.g., Cα–C′ → Cα–C=C–C′), β-amino acids (e.g., Cα–C′ → Cα–CH2–C′ or Cα–CHR-C′ wherein R is a side-chain independent or intramolecularly cyclized to the Cα-carbon), aza-amino acids (e.g., Cα → N) and achiral N-substituted Gly or “peptoid” (e.g., Nα–Cα–C′ → NR–CH2–C′ wherein R is a side-chain). Furthermore, the Cβ-carbon may be substituted to create “chimeric” amino acids. Overall, such Nα–Cα–C and/or Cα–Cβ scaffold modifications provide significant opportunities to enhance peptide-like chemical diversity relative to peptide ϕ–ψ–χ space.

The peptide backbone has been the focus of significant chemical modifications over the past three decades to impart varying desired properties, including (i) increased metabolic stability against proteolytic degradation; (ii) enhanced molecular recognition for active site binding in designing peptidomimetic inhibitors of protease therapeutic targets; and (iii) improved bioavailability. Examples of amide bond replacements that have been described within the scope of peptide and peptidomimetic drug discovery6,20,129–132  include aminomethylene (CH2NH), ketomethylene (COCH2), ethylene (CH2CH2), olefin (CH=CH), ether (CH2O), thioether (CH2S), tetrazole, thiazole, retroamide (NHCO), thioamide and ester. Other non-hydrolyzable amide bond isosteres have been leveraged for protease inhibitor drug design,6,13,20,33,133,134  including hydroxymethylene (CH[OH]), hydroxyethylene (CH[OH]CH2), dihydroxyethylene (CH[OH]CH[OH]), hydroxyethylamine (CH[OH]CH2N) and related C2-symmetric hydroxymethylene and dihydroxyethylene moieties. Some marketed HIV protease inhibitors (e.g., amprenavir, indinavir, saquinavir, darunavir, atazanavir and nelfinavir133,134 ) have successfully exploited such non-hydrolyzable amide bond isosteres (Figure 1.9) and further underscore the greater scope of the second wave of peptide drug discovery, in terms of both peptidomimetic design and tackling intracellular target space.

Figure 1.9

Structures of some marketed HIV-1 protease inhibitors.

Figure 1.9

Structures of some marketed HIV-1 protease inhibitors.

Close modal

An understanding of 3D structural and conformational properties of peptides that correlates with their binding and modulation of therapeutic targets has inspired creativity in peptide drug design.6–8,10,12,15,16,18,20,68–79,135–152  Historically, regiospecific substitution of d-amino acids, Nα-Me-amino acids, Cα-Me-amino acids, dehydro-amino acids and cyclic amino acids within a peptide lead compound have provided insights relative to their propensities to induce or stabilize β-turn, γ-turn, β-strand, α-helix and 310-helix secondary structures. Furthermore, the progression of versatile synthetic peptidomimetics and designed non-peptide templates that attempt to incorporate key side-chain and/or backbone elements of such peptide secondary structures is noteworthy (see later).

The β-turn was often inferred from structure–activity analysis to be a critical secondary structural element within class A GPCR peptide pharmacophores (e.g., somatostatin, α-melanotropin and enkephalin).4–6,9–11,16,19,38–40,118,136,141,147,148,153  The β-turn motif exists within tetrapeptide sequences in which the first and fourth Cα atoms are separated by ≤7 Å and a 10-membered intramolecular H-bond exists between the i and i + 4 amino acid residues. Noteworthy are monocyclic or bicyclic templates (1–4) (Figure 1.10) from early efforts focused on designed β-turn peptidomimetics.154–157 

Figure 1.10

Some examples of peptidomimetic scaffolds and designed non-peptide templates advanced to explore the β-turn peptide motif.

Figure 1.10

Some examples of peptidomimetic scaffolds and designed non-peptide templates advanced to explore the β-turn peptide motif.

Close modal

The β-strand motif has been recognized with respect to the molecular recognition of both peptide substrate-based inhibitors of proteases and at the interfaces of numerous protein–protein interactions.16,89,90,109,118,137,145,148–150  Noteworthy are the peptidomimetic scaffold and non-peptide templates (5–8) (Figure 1.11) that have been advanced as designed β-strand mimetics.149,158–161 

Figure 1.11

Some examples of peptidomimetic scaffolds and designed non-peptide templates advanced to explore the β-strand peptide motif.

Figure 1.11

Some examples of peptidomimetic scaffolds and designed non-peptide templates advanced to explore the β-strand peptide motif.

Close modal

The α-helix is a major secondary structure found in nature, and exists at the interface of a plethora of protein–protein interactions.12,16,18,36,37,68–73,86–88,91,118,151,152  Such α-helices may be categorized as linear, kinked and curved, of which the latter is the predominant type. Noteworthy are peptidomimetic scaffold and non-peptide templates (9–12) (Figure 1.12) that have been advanced as designed α-helical mimetics.162–165  Furthermore, peptidomimetics generated from other building blocks, including N-substituted Gly (“peptoid”) and β-amino acids (“β-peptides”), have been shown to exhibit helical-type secondary structures.166–169 

Figure 1.12

Some examples of peptidomimetic scaffolds and designed non-peptide templates advanced to explore the α-helix peptide motif.

Figure 1.12

Some examples of peptidomimetic scaffolds and designed non-peptide templates advanced to explore the α-helix peptide motif.

Close modal

Over recent years, peptide macrocyclization has resurged as a key driving force of the third wave of peptide drug discovery in terms of leveraging both structure-based design and super-diverse library screening of synthetic peptides to accelerate their generation, optimization and development.68–79,84,85,91,109,113–118,170–175  The scope of chemical space is remarkable and provides significant opportunity for peptide drug discovery focused on intracellular targets such as those exemplified by known macrocyclic peptide natural products (e.g., cyclosporine A or CsA, 13176 ), designed macrocyclic peptidomimetic inhibitors of proteases (e.g., hepatitis C virus (HCV) protease inhibitor 14177 ), designed stapled α-helical peptides (e.g., dual MDM2/X antagonist 15178 ), CsA-inspired mRNA display library-generated macrocyclic peptides (e.g., SIRT2 inhibitor 16179 ), CsA-inspired DNA encoded library-generated macrocyclic peptides (e.g., XIAP antagonist 17180 ) and cell-penetrating peptide (CPP) hybridized synthetic library-generated macrocyclic peptides (e.g., KRAS antagonist 18181 ) (Figure 1.13). Collectively, such macrocyclic peptides are providing novel chemotypes with unique biophysical and pharmacological properties to tackle so-called “undruggable” targets. In particular, there has been significant progress in the study of α-helical peptides,68–73,91,118,171–175,178  including progression into the clinic.

Figure 1.13

Examples of macrocyclic peptides of varying chemotypes.

Figure 1.13

Examples of macrocyclic peptides of varying chemotypes.

Close modal

Without question, the revival of macrocyclic peptide drug discovery has been led by the conception of stapled α-helical peptides and the application of varying synthetic chemistries (e.g., ring-closing metathesis, azide–alkyne cycloaddition and thioether) such as those exemplified (Figure 1.14) by macrocyclic α-helical peptides (19–22),182–185  the first described MDM2 and/or MDMX antagonists of their particularly unique chemotype. Noteworthy is the highly potent and in vivo effective dual MDM2 and MDMX antagonist ATSP-7041 (15, Figure 1.13), which has become a benchmark stapled peptide relative to its intrinsic biophysical properties (e.g., amphipathicity, solubility, cell permeability and metabolic stability178,186 ) that are implicated as key features to design concepts for this class of macrocyclic α-helical peptides.71–73,171  Of note, such work has successfully translated to the first stapled α-helical peptide for an intracellular target to advance into the clinic (ALRN-6924 by Aileron Therapeutics). Collectively, and related to the first publication187  of stapled α-helical peptides incorporating α-methyl amino acids as part of the macrocyclization framework, there are now many reported studies illuminating the design, chemistry and biological properties of stapled peptides for a wide range of intracellular protein–protein interaction targets (e.g., Bcl-2 family, Notch, estrogen receptor, vitamin D receptor, HIV capsid, HIV integrase, HIF-1, KRAS, Rab, p53, β-catenin, replication protein A, eIF4E, WASF3 and aurora-A kinase178,182–221 ). Nevertheless, there is more to understand with respect to cell permeability and drug delivery of peptides (see later) to fully exploit this macrocyclic α-helical peptide modality class relative to tackling intracellular target space and the translation to clinical development.

Figure 1.14

Examples of p53 proteomimetics having MDM2 and MDMX binding properties and highlighting varying macrocyclization chemistries.

Figure 1.14

Examples of p53 proteomimetics having MDM2 and MDMX binding properties and highlighting varying macrocyclization chemistries.

Close modal

Beyond the aforementioned macrocyclic α-helical peptide drug discovery efforts there has been extraordinary progress over recent years with respect to exploiting super-diverse mRNA display,74–78  phage display,79,80  DNA-encoded81  and synthetic macrocyclic (e.g., CPP) peptide libraries.84,85  Such super-diverse libraries may explore virtually all possible canonical and non-canonical peptide conformational space, generally relative to within a 8–20 amino acid size range, although with the inclusion of multiple cyclization designs there is opportunity to introduce further constraints to molecular flexibility (Figure 1.13). In the case of mRNA display, DNA-encoded peptide and other synthetic macrocyclic peptide libraries, the inclusion of unnatural amino acids (e.g., N-methyl-amino acids, Cα-methyl-amino acids, d-amino acids, β-amino acids and further novelties relative to side-chain functionality) and/or other structurally related building blocks may be incorporated.74–78,81,84,85  In the case of phage display libraries, the use of thioether bridging of Cys residues79  provides the opportunity to explore diversity (e.g., peptide sequence, ring-size and varying linking moieties), and more recently the synthesis of phage-generated glycopeptide libraries80  illustrates further innovative chemistry methods. Lastly, but not exactly fitting into the realm of super-diverse libraries, is the more recent focused study of smaller macrocyclic peptides (i.e., five- and six-membered rings) to explore CsA-like passive permeability relative to systematic modification of both backbone and side-chain functionalities, and then leverage such structure–property relationships to identify peptide scaffolds showing cell permeability (see later) may then be further designed to achieve molecular recognition for therapeutic targets.222–227 

As the saying goes, “this is where the rubber hits the road”; it is a fitting introductory comment, especially in discussing peptide cell permeability and drug delivery, as these have been major challenges to overcome and unleash the potential of the peptide modality. In terms of addressing this topic, peptides traverse cell membranes in two ways: passively or non-passively. This oversimplification of what is unquestionably complex nevertheless guides peptide drug discovery strategies focused on integrating screening tools and advancing design rules for peptide cell permeability. What is the strategic modus operandi? It begins with learning from what nature has revealed and the use of empirical methods to systematically explore peptide structure–property relationships as well as delineating the cellular mechanisms of membrane permeability. With respect to passive permeability, cyclosporine A has provided a benchmark for designing macrocyclic peptides with oral bioavailability.176,222–230  In contrast, with respect to non-passive permeability, Tat and Tat-related polycationic CPPs have provided benchmarks and tools to understand linear peptides exhibiting cell uptake by endocytosis and/or other translocation mechanisms.231–251  Beyond linear CPPs, noteworthy progress is being achieved with stapled α-helical peptides, and more recently, CPP-hybrid macrocyclic peptides relative to exploring their structure–permeability relationships to optimize their cellular potency to modulate intracellular targets as well as further understand the their cell uptake mechanisms.84,85,181  In the specific case of stapled α-helical peptides, some key properties that have been proposed71–73,171,174,178  to correlate with cellular potency include amphipathicity (predominantly hydrophobic, but with preservation of solubility), lipophilicity (partitioning into membranes), α-helicity (intramolecular H-bonding), proteolytic stability and high binding affinity to their cognate target (Kd < 1–10 nM range) to their respective cognate targets (Figure 1.15). Therefore, iterative design and analysis of peptide structure–permeability relationships may address intrinsic physicochemical/biophysical properties as well as experimental permeability properties using cellular and related non-cellular model systems. It is likely that comparative analysis of linear CPPs, CPP-hybrid macrocyclic peptides and/or stapled α-helical peptides will provide insight with which to further benchmark and more deeply understand peptide cell uptake, including relationships to endocytosis and/or other translocation mechanisms. Recent studies have addressed this relative to comparative analysis of linear CPPs with CPP-hybrid macrocyclic peptides,84  as well as comparative analysis of linear CPPs with stapled α-helical peptides.252  Furthermore, in the case of CPP-hybrid macrocyclic peptides it was demonstrated that such molecules were capable of high cytosolic delivery efficiency, so that cell uptake included direct interaction with plasma membrane phosopholipids, endocytosis and subsequent release from endosomes.84  In addition, this study revealed that such molecules induced membrane curvature on giant unilamellar vesicles and budding of small vesicles that subsequently collapsed into amorphous aggregates of peptide and lipid. In contrast, an evaluation of structurally varied stapled α-helical peptides implicated a correlation of cell uptake via a clathrin- and caveolin-independent endocytosis pathway that was suggested to involve, in part, anionic cell surface proteoglycans.252 

Figure 1.15

Stapled peptide cell uptake working model to explore non-passive mechanisms as well as analog optimization relative to physicochemical, biophysical and biological properties (functional activity and cell permeability).

Figure 1.15

Stapled peptide cell uptake working model to explore non-passive mechanisms as well as analog optimization relative to physicochemical, biophysical and biological properties (functional activity and cell permeability).

Close modal

To understand more deeply peptide cell permeability and drug delivery it is critically essential to have screening tools, including both computational and experimental methods, which can facilitate predictive design rules and iterative testing thereof. Unsurprisingly, for passive permeability there exists a framework of existing screening tools (e.g., transcellular permeability using cellular monolayers and proteolytic stability in varying biological matrices) as well as emerging computational and biophysical methods222–227,253–260  that address exposed polar surface properties, NMR/mass spectroscopy (MS) analysis of intramolecular H-bonding, radius of gyration and aqueous/lipid phase partitioning (Table 1.2). Furthermore, cell-based screening methods261–265  utilizing MS and fluorescence microscopy for unlabeled or fluorescently labeled peptides, respectively, may be implemented to determine cell uptake as well as provide correlation with their cellular functional properties (Table 1.2). For non-passive permeability, it is likely that some (perhaps most) of the same physicochemical and biophysical properties will be important, since peptide partitioning into the cell membrane will have similar requirements for hydrophobicity/lipophilicity and desolvation of exposed H-bonding moieties of the peptide molecule. Certainly, there is ample opportunity for the development of innovative permeability screening tools to enable peptide drug discovery and it is anticipated that such tools will continue to emerge in the future.

Table 1.2

Screening tools to study peptide permeability (passive and non-passive)

Screening ToolCommentsReference
Exposed polar surface area (EPSA) Experimental EPSA values are determined using supercritical fluid chromatography. Low EPSA values have been shown to correlate with high passive permeability and predicted oral bioavailability 253 and 259  
ΔG(insertion) ΔG(insertion) is a calculated value that refers to the free energy for transferring peptide in a low-dielectric conformation (LDC) from water to a low-dielectric environment (membrane-like) to be predictive of passive permeability 254 and 259  
PAMPA Parallel artificial membrane permeability (PAMPA) uses mixtures of phospholipids infused into lipophilic microfilters with net negative charge (surrogate model system to correlate with experimental oral bioavailability) 222,224,256,259  
Lipid:water partitioning Both octanol:water partitioning and recent modification to incorporate fatty acids and pH gradient as shown for Arg-rich peptides to be predictive of energy-independent translocation (non-passive permeability) 260  
Cell monolayer trancytosis Caco-2 cells (or other cell types) used to measure permeability from donor to acceptor compartments and further correlated with experimental oral bioavailability 253 and 259  
Radius of gyration R-gyr is calculated as the root-mean-square distance between the molecule’s atoms and its center of gravity. It is an alternative property for MW for beyond-rule-of-5 molecules 258  
NMR analysis of intramolecular H-bonding versus solvent H-bonding Solution NMR studies using hydrogen/deuterium (H/D) exchange experiments to determine amide temperature coefficients and intramolecular hydrogen bonding 254 and 256  
Label-free mass spectrometric and fluorescently-tagged cell uptake and permeability analysis Direct measurement methods for cell uptake of peptides using MS methods and/or imaging studies (e.g., fluorescence correlation microscopy) to correlate with cell-based functional assays as well as non-cell-based surrogate models 261–265  
Screening ToolCommentsReference
Exposed polar surface area (EPSA) Experimental EPSA values are determined using supercritical fluid chromatography. Low EPSA values have been shown to correlate with high passive permeability and predicted oral bioavailability 253 and 259  
ΔG(insertion) ΔG(insertion) is a calculated value that refers to the free energy for transferring peptide in a low-dielectric conformation (LDC) from water to a low-dielectric environment (membrane-like) to be predictive of passive permeability 254 and 259  
PAMPA Parallel artificial membrane permeability (PAMPA) uses mixtures of phospholipids infused into lipophilic microfilters with net negative charge (surrogate model system to correlate with experimental oral bioavailability) 222,224,256,259  
Lipid:water partitioning Both octanol:water partitioning and recent modification to incorporate fatty acids and pH gradient as shown for Arg-rich peptides to be predictive of energy-independent translocation (non-passive permeability) 260  
Cell monolayer trancytosis Caco-2 cells (or other cell types) used to measure permeability from donor to acceptor compartments and further correlated with experimental oral bioavailability 253 and 259  
Radius of gyration R-gyr is calculated as the root-mean-square distance between the molecule’s atoms and its center of gravity. It is an alternative property for MW for beyond-rule-of-5 molecules 258  
NMR analysis of intramolecular H-bonding versus solvent H-bonding Solution NMR studies using hydrogen/deuterium (H/D) exchange experiments to determine amide temperature coefficients and intramolecular hydrogen bonding 254 and 256  
Label-free mass spectrometric and fluorescently-tagged cell uptake and permeability analysis Direct measurement methods for cell uptake of peptides using MS methods and/or imaging studies (e.g., fluorescence correlation microscopy) to correlate with cell-based functional assays as well as non-cell-based surrogate models 261–265  

A compelling future of peptide drug discovery is envisaged relative to the promise of novel breakthrough medicines that are no longer restricted to extracellular or receptor targets, but rather overcome permeability barriers to enable the prosecution of intracellular targets, especially those generally perceived as undruggable. As highlighted in this chapter, there is robust knowledge that reflects more several decades of pioneering multidisciplinary science and technology on the peptide modality. This has contributed to hierarchical design strategies, expansive chemical space, sophisticated computational modeling, and a plethora of high-resolution crystal structures of peptide complexes with their cognate targets. Super-diverse peptide libraries generated by mRNA display, phage display, DNA-encoding and other synthetic chemistry methods exemplify a disruptive innovation that will impact both basic research and translation to clinical development. Collectively, several key technology platforms (Table 1.3) that are enabling such structure-based design and library-driven diversity of macrocyclic peptides are acknowledged with respect to contributing to such disruptive innovation.

Table 1.3

Macrocylic peptide technology platforms highlighting varying macrocylization chemistries

CompanyChemistry platformR&D pipeline (internal/external)
Aileron therapeutics Stapled helical peptides (multiple chemistries) Dual MDM2/MDMX antagonist ALRN-6924 (phase 1/phase 2); GHRH agonist ALRN-5281 (phase 1) 
Bicycle therapeutics Phage display of bicyclic peptides Undisclosed (preclinical) 
Circle pharma Cell permeable macrocyclic peptides Undisclosed (preclinical); Pfizer collaboration 
Encycle therapeutics Amphoteric cyclization (nacellins) Multiple partnerships (preclinical) 
Ensemble therapeutics DNA-programmed macrocycles IL-17 antagonist (partnered with Novartis) and several pharma collaborations (preclinical) 
Fog pharma Cell permeable mini-proteins Undisclosed (preclinical) 
Pepscan therapeutics Chemical linkage of peptides (CLIPS) into scaffolds HIV fusion inhibitor (partnered with Crucell); IBD peptide (partnered with Zealand) 
PeptiDream RNA display of modified cyclic peptides Flu inhibitor (partnered with TMI); Merck & Co and several other pharma collaborations (preclinical) 
Polyphor MacoFinder/PEMfinder platforms for macrocycles and cyclic peptides CXCR4 antagonist POL6326 (phase 2); Antibiotic POL7080 (phase 2); Gilead collaboration (preclinical) 
Protagonist therapeutics Disulfide-rich peptides (DRPs) α4β7 antagonist (preclinical); IL-6 antagonist (partnered with Ironwood Pharmaceuticals) 
Ra Pharmaceuticals RNA display of modified cyclic peptides (Cyclomimetics™) Complement C5 inhibitor RA101495 (phase 1); Merck & Co. partnership (preclinical) 
CompanyChemistry platformR&D pipeline (internal/external)
Aileron therapeutics Stapled helical peptides (multiple chemistries) Dual MDM2/MDMX antagonist ALRN-6924 (phase 1/phase 2); GHRH agonist ALRN-5281 (phase 1) 
Bicycle therapeutics Phage display of bicyclic peptides Undisclosed (preclinical) 
Circle pharma Cell permeable macrocyclic peptides Undisclosed (preclinical); Pfizer collaboration 
Encycle therapeutics Amphoteric cyclization (nacellins) Multiple partnerships (preclinical) 
Ensemble therapeutics DNA-programmed macrocycles IL-17 antagonist (partnered with Novartis) and several pharma collaborations (preclinical) 
Fog pharma Cell permeable mini-proteins Undisclosed (preclinical) 
Pepscan therapeutics Chemical linkage of peptides (CLIPS) into scaffolds HIV fusion inhibitor (partnered with Crucell); IBD peptide (partnered with Zealand) 
PeptiDream RNA display of modified cyclic peptides Flu inhibitor (partnered with TMI); Merck & Co and several other pharma collaborations (preclinical) 
Polyphor MacoFinder/PEMfinder platforms for macrocycles and cyclic peptides CXCR4 antagonist POL6326 (phase 2); Antibiotic POL7080 (phase 2); Gilead collaboration (preclinical) 
Protagonist therapeutics Disulfide-rich peptides (DRPs) α4β7 antagonist (preclinical); IL-6 antagonist (partnered with Ironwood Pharmaceuticals) 
Ra Pharmaceuticals RNA display of modified cyclic peptides (Cyclomimetics™) Complement C5 inhibitor RA101495 (phase 1); Merck & Co. partnership (preclinical) 

To conclude, the renaissance of peptide drug discovery has reignited the torch of both chemistry and biology to converge upon intracellular target space with an armamentarium of structurally diverse macrocyclic peptides along with cell permeability screening tools and design rules to advance the peptide modality. This is the third wave, and it is growing with force and momentum to be a powerful one.

I wish to acknowledge my colleagues at Merck & Co. (Kenilworth, NJ, USA) who have been actively engaged within the Peptide Drug Hunter network, and I am grateful for their many contributions and achievements. Likewise, I wish to acknowledge our collaborations with Ra Pharmaceuticals, PeptiDream, A*STAR and IRBM to advance our peptide drug modality pipeline.

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Figures & Tables

Figure 1.1

Some key milestones in peptide science and technology. Several “waves” of peptide and peptidomimetic drug discovery over the past three decades are highlighted.

Figure 1.1

Some key milestones in peptide science and technology. Several “waves” of peptide and peptidomimetic drug discovery over the past three decades are highlighted.

Close modal
Figure 1.2

Human genome mapping of the G protein-coupled receptor (GPCR) superfamily.

Figure 1.2

Human genome mapping of the G protein-coupled receptor (GPCR) superfamily.

Close modal
Figure 1.3

Amino acid sequences of several known G protein-coupled receptor (GPCR)-targeted marketed peptide drugs that further show β-turn and α-helix substructural motifs proposed for their bioactive conformations.

Figure 1.3

Amino acid sequences of several known G protein-coupled receptor (GPCR)-targeted marketed peptide drugs that further show β-turn and α-helix substructural motifs proposed for their bioactive conformations.

Close modal
Figure 1.4

Structures of [Cys4,10]α-MSH, Ac-cyclo[Nle4, Asp5, d-Phe7, Lys10]α-MSH4-10-NH2, setmelanotide and cyclo[Nle4, Asp5, d-Nal(2′)7, Lys10]α-MSH4-10-NH2 highlighting the β-turn conformations about the central His-Phe-Arg-Trp sequence of MSH.

Figure 1.4

Structures of [Cys4,10]α-MSH, Ac-cyclo[Nle4, Asp5, d-Phe7, Lys10]α-MSH4-10-NH2, setmelanotide and cyclo[Nle4, Asp5, d-Nal(2′)7, Lys10]α-MSH4-10-NH2 highlighting the β-turn conformations about the central His-Phe-Arg-Trp sequence of MSH.

Close modal
Figure 1.5

Structures of some marketed or clinically advanced (semaglutide) glucagon-like peptide (GLP)-1 agonists.

Figure 1.5

Structures of some marketed or clinically advanced (semaglutide) glucagon-like peptide (GLP)-1 agonists.

Close modal
Figure 1.6

Some representative protein–protein interactions highlighting α-helical interfaces.

Figure 1.6

Some representative protein–protein interactions highlighting α-helical interfaces.

Close modal
Figure 1.7

Hierarchical drug design strategies to advance novel peptide, peptidomimetic and proteomimetic drugs for extracellular/receptor and intracellular targets.

Figure 1.7

Hierarchical drug design strategies to advance novel peptide, peptidomimetic and proteomimetic drugs for extracellular/receptor and intracellular targets.

Close modal
Figure 1.8

3D perspective of peptide structural and conformation properties.

Figure 1.8

3D perspective of peptide structural and conformation properties.

Close modal
Figure 1.9

Structures of some marketed HIV-1 protease inhibitors.

Figure 1.9

Structures of some marketed HIV-1 protease inhibitors.

Close modal
Figure 1.10

Some examples of peptidomimetic scaffolds and designed non-peptide templates advanced to explore the β-turn peptide motif.

Figure 1.10

Some examples of peptidomimetic scaffolds and designed non-peptide templates advanced to explore the β-turn peptide motif.

Close modal
Figure 1.11

Some examples of peptidomimetic scaffolds and designed non-peptide templates advanced to explore the β-strand peptide motif.

Figure 1.11

Some examples of peptidomimetic scaffolds and designed non-peptide templates advanced to explore the β-strand peptide motif.

Close modal
Figure 1.12

Some examples of peptidomimetic scaffolds and designed non-peptide templates advanced to explore the α-helix peptide motif.

Figure 1.12

Some examples of peptidomimetic scaffolds and designed non-peptide templates advanced to explore the α-helix peptide motif.

Close modal
Figure 1.13

Examples of macrocyclic peptides of varying chemotypes.

Figure 1.13

Examples of macrocyclic peptides of varying chemotypes.

Close modal
Figure 1.14

Examples of p53 proteomimetics having MDM2 and MDMX binding properties and highlighting varying macrocyclization chemistries.

Figure 1.14

Examples of p53 proteomimetics having MDM2 and MDMX binding properties and highlighting varying macrocyclization chemistries.

Close modal
Figure 1.15

Stapled peptide cell uptake working model to explore non-passive mechanisms as well as analog optimization relative to physicochemical, biophysical and biological properties (functional activity and cell permeability).

Figure 1.15

Stapled peptide cell uptake working model to explore non-passive mechanisms as well as analog optimization relative to physicochemical, biophysical and biological properties (functional activity and cell permeability).

Close modal
Table 1.1

Some peptide and peptidomimetic drugs approved by the United States Food and Drug Administration (FDA) or European Medicines Agency (EMA)

Trade name (INN)Therapeutic targetPrimary useApproval
DDAVP (desmopressin) Vasopressin receptora Diabetes insipidus 1978 (FDA) 
Sandimmune (cyclosporine) Cyclophilin/calcineurinb Immunotherapy 1983 (FDA) 
Lupron (leuprorelin) GnRH receptora Oncology 1985 (FDA) 
Zoladex (goserelin) GnRH receptora Oncology 1989 (FDA) 
Invirase (saquinavir) HIV-1 proteaseb Infectious disease 1996 (FDA) 
Copaxone (glatiramer) T-cell functiona Allergy, immunology 1996 (FDA) 
Crixivan (indinavir) HIV-1 proteaseb Infectious disease 1996 (FDA) 
Viracept (nelfinavir) HIV-1 proteaseb Infectious disease 1997 (FDA) 
GlucaGen (recombinant glucagon) Glucagon receptora Metabolic 1998 (FDA) 
Integrilin (eptifibatide) Integrin receptora Cardiovascular 1998 (FDA) 
Sandostatin (octreotide) Somatostatin receptora Acromegly 1998 (FDA) 
Angiomax (bivalirudin) Thrombina Hematology 2000 (FDA) 
Agenerase (amprenavir) HIV-1 proteaseb Infectious disease 1999 (FDA) 
Cetrotide (cetrorelix) GnRH receptora Endocrinology 2000 (FDA) 
Trelstar (triptorelin) GnRH receptora Oncology 2000 (FDA) 
Natrecor (nesiritide) Natriuretic peptide receptora Cardiovascular 2001 (FDA) 
Byetta (exenatide) Glucagon-like peptide-1 receptora Metabolic 2002 (FDA) 
Forteo (teriparatide) Parathyroid hormone receptora Metabolic 2002 (FDA) 
Neulasta (pegfilgrastim) G-CSF receptora Oncology 2002 (FDA) 
Reyataz (atazanavir) HIV-1 proteaseb Infectious disease 2003 (FDA) 
Cubicin (daptomycin) Bacterial cell membranec Antibacterial 2003 (FDA) 
Fuzeon (enfuvirtide) gp41 of HIV fusion complexa Infectious disease 2003 (FDA) 
Plenaxis (abarelix) GnRH receptora Oncology 2003 (FDA) 
Velcade (bortezomib) 26S proteasomeb Oncology 2003 (FDA) 
Prialt (ziconotide) N-type calcium channela Central nervous system 2004 (FDA) 
Symlin (pramlintide) Amylin receptora Metabolic 2005 (FDA) 
Vantas (histrelin) GnRH receptora Oncology 2005 (FDA) 
Prezista (darunavir) HIV-1 proteaseb Infectious disease 2006 (FDA) 
Somatuline (lanreotide) Somatostatin receptora Endocrinology 2007 (FDA) 
Firmagon (degarelix) GnRH receptora Oncology 2009 (FDA) 
Victoza (liraglutide) Glucagon-like peptide-1 receptora Metabolic 2010 (FDA) 
Linzess (linaclotide) Guanylate cyclase receptora Gastrointestinal 2012 (FDA) 
Signifor (pasireotide) Somatostatin receptora Cushing’s disease 2012 (FDA) 
Gattex (teduglutide) Glucagon-like peptide-2 receptora Gastrointestinal 2012 (FDA) 
Kyprolis (carfilzomib) Proteasomeb Oncology 2012 (FDA) 
Scenesse (afamelanotide) Melanocortin-1 receptora Skin pigmentation 2014 (EMA) 
Afrezza (inhaled insulin) Insulin receptora Diabetes 2014 (FDA) 
Saxenda (liraglutide) Glucagon-like peptide-1 receptora Metabolic 2014 (FDA) 
Trulicity (dulaglutide) Glucagon-like peptide-1 receptora Metabolic 2014 (FDA) 
Ninlaro (ixazomib) Proteasomeb Oncology 2015 (FDA) 
Pabal (carbetocin) Oxytocin receptora Obstetrics 2015 (EMA) 
Natpara (parathyroid hormone) Parathyroid hormone receptora Hypocalcemia 2015 (FDA) 
Toujeo (insulin glargine) Insulin receptora Diabetes 2015 (FDA) 
Tresiba (insulin degludec) Insulin receptora Diabetes 2015 (FDA) 
Adlyxin (lixisenatide) Glucagon-like peptide-1 receptora Metabolic 2016 (FDA) 
Zepatier (grazoprevir) HCV proteaseb Infectious disease 2016 (FDA) 
Trade name (INN)Therapeutic targetPrimary useApproval
DDAVP (desmopressin) Vasopressin receptora Diabetes insipidus 1978 (FDA) 
Sandimmune (cyclosporine) Cyclophilin/calcineurinb Immunotherapy 1983 (FDA) 
Lupron (leuprorelin) GnRH receptora Oncology 1985 (FDA) 
Zoladex (goserelin) GnRH receptora Oncology 1989 (FDA) 
Invirase (saquinavir) HIV-1 proteaseb Infectious disease 1996 (FDA) 
Copaxone (glatiramer) T-cell functiona Allergy, immunology 1996 (FDA) 
Crixivan (indinavir) HIV-1 proteaseb Infectious disease 1996 (FDA) 
Viracept (nelfinavir) HIV-1 proteaseb Infectious disease 1997 (FDA) 
GlucaGen (recombinant glucagon) Glucagon receptora Metabolic 1998 (FDA) 
Integrilin (eptifibatide) Integrin receptora Cardiovascular 1998 (FDA) 
Sandostatin (octreotide) Somatostatin receptora Acromegly 1998 (FDA) 
Angiomax (bivalirudin) Thrombina Hematology 2000 (FDA) 
Agenerase (amprenavir) HIV-1 proteaseb Infectious disease 1999 (FDA) 
Cetrotide (cetrorelix) GnRH receptora Endocrinology 2000 (FDA) 
Trelstar (triptorelin) GnRH receptora Oncology 2000 (FDA) 
Natrecor (nesiritide) Natriuretic peptide receptora Cardiovascular 2001 (FDA) 
Byetta (exenatide) Glucagon-like peptide-1 receptora Metabolic 2002 (FDA) 
Forteo (teriparatide) Parathyroid hormone receptora Metabolic 2002 (FDA) 
Neulasta (pegfilgrastim) G-CSF receptora Oncology 2002 (FDA) 
Reyataz (atazanavir) HIV-1 proteaseb Infectious disease 2003 (FDA) 
Cubicin (daptomycin) Bacterial cell membranec Antibacterial 2003 (FDA) 
Fuzeon (enfuvirtide) gp41 of HIV fusion complexa Infectious disease 2003 (FDA) 
Plenaxis (abarelix) GnRH receptora Oncology 2003 (FDA) 
Velcade (bortezomib) 26S proteasomeb Oncology 2003 (FDA) 
Prialt (ziconotide) N-type calcium channela Central nervous system 2004 (FDA) 
Symlin (pramlintide) Amylin receptora Metabolic 2005 (FDA) 
Vantas (histrelin) GnRH receptora Oncology 2005 (FDA) 
Prezista (darunavir) HIV-1 proteaseb Infectious disease 2006 (FDA) 
Somatuline (lanreotide) Somatostatin receptora Endocrinology 2007 (FDA) 
Firmagon (degarelix) GnRH receptora Oncology 2009 (FDA) 
Victoza (liraglutide) Glucagon-like peptide-1 receptora Metabolic 2010 (FDA) 
Linzess (linaclotide) Guanylate cyclase receptora Gastrointestinal 2012 (FDA) 
Signifor (pasireotide) Somatostatin receptora Cushing’s disease 2012 (FDA) 
Gattex (teduglutide) Glucagon-like peptide-2 receptora Gastrointestinal 2012 (FDA) 
Kyprolis (carfilzomib) Proteasomeb Oncology 2012 (FDA) 
Scenesse (afamelanotide) Melanocortin-1 receptora Skin pigmentation 2014 (EMA) 
Afrezza (inhaled insulin) Insulin receptora Diabetes 2014 (FDA) 
Saxenda (liraglutide) Glucagon-like peptide-1 receptora Metabolic 2014 (FDA) 
Trulicity (dulaglutide) Glucagon-like peptide-1 receptora Metabolic 2014 (FDA) 
Ninlaro (ixazomib) Proteasomeb Oncology 2015 (FDA) 
Pabal (carbetocin) Oxytocin receptora Obstetrics 2015 (EMA) 
Natpara (parathyroid hormone) Parathyroid hormone receptora Hypocalcemia 2015 (FDA) 
Toujeo (insulin glargine) Insulin receptora Diabetes 2015 (FDA) 
Tresiba (insulin degludec) Insulin receptora Diabetes 2015 (FDA) 
Adlyxin (lixisenatide) Glucagon-like peptide-1 receptora Metabolic 2016 (FDA) 
Zepatier (grazoprevir) HCV proteaseb Infectious disease 2016 (FDA) 
a

Extracellular/receptor therapeutic targets.

b

intracellular therapeutic targets.

c

antibiotic peptides that disruptive bacterial membranes.

Table 1.2

Screening tools to study peptide permeability (passive and non-passive)

Screening ToolCommentsReference
Exposed polar surface area (EPSA) Experimental EPSA values are determined using supercritical fluid chromatography. Low EPSA values have been shown to correlate with high passive permeability and predicted oral bioavailability 253 and 259  
ΔG(insertion) ΔG(insertion) is a calculated value that refers to the free energy for transferring peptide in a low-dielectric conformation (LDC) from water to a low-dielectric environment (membrane-like) to be predictive of passive permeability 254 and 259  
PAMPA Parallel artificial membrane permeability (PAMPA) uses mixtures of phospholipids infused into lipophilic microfilters with net negative charge (surrogate model system to correlate with experimental oral bioavailability) 222,224,256,259  
Lipid:water partitioning Both octanol:water partitioning and recent modification to incorporate fatty acids and pH gradient as shown for Arg-rich peptides to be predictive of energy-independent translocation (non-passive permeability) 260  
Cell monolayer trancytosis Caco-2 cells (or other cell types) used to measure permeability from donor to acceptor compartments and further correlated with experimental oral bioavailability 253 and 259  
Radius of gyration R-gyr is calculated as the root-mean-square distance between the molecule’s atoms and its center of gravity. It is an alternative property for MW for beyond-rule-of-5 molecules 258  
NMR analysis of intramolecular H-bonding versus solvent H-bonding Solution NMR studies using hydrogen/deuterium (H/D) exchange experiments to determine amide temperature coefficients and intramolecular hydrogen bonding 254 and 256  
Label-free mass spectrometric and fluorescently-tagged cell uptake and permeability analysis Direct measurement methods for cell uptake of peptides using MS methods and/or imaging studies (e.g., fluorescence correlation microscopy) to correlate with cell-based functional assays as well as non-cell-based surrogate models 261–265  
Screening ToolCommentsReference
Exposed polar surface area (EPSA) Experimental EPSA values are determined using supercritical fluid chromatography. Low EPSA values have been shown to correlate with high passive permeability and predicted oral bioavailability 253 and 259  
ΔG(insertion) ΔG(insertion) is a calculated value that refers to the free energy for transferring peptide in a low-dielectric conformation (LDC) from water to a low-dielectric environment (membrane-like) to be predictive of passive permeability 254 and 259  
PAMPA Parallel artificial membrane permeability (PAMPA) uses mixtures of phospholipids infused into lipophilic microfilters with net negative charge (surrogate model system to correlate with experimental oral bioavailability) 222,224,256,259  
Lipid:water partitioning Both octanol:water partitioning and recent modification to incorporate fatty acids and pH gradient as shown for Arg-rich peptides to be predictive of energy-independent translocation (non-passive permeability) 260  
Cell monolayer trancytosis Caco-2 cells (or other cell types) used to measure permeability from donor to acceptor compartments and further correlated with experimental oral bioavailability 253 and 259  
Radius of gyration R-gyr is calculated as the root-mean-square distance between the molecule’s atoms and its center of gravity. It is an alternative property for MW for beyond-rule-of-5 molecules 258  
NMR analysis of intramolecular H-bonding versus solvent H-bonding Solution NMR studies using hydrogen/deuterium (H/D) exchange experiments to determine amide temperature coefficients and intramolecular hydrogen bonding 254 and 256  
Label-free mass spectrometric and fluorescently-tagged cell uptake and permeability analysis Direct measurement methods for cell uptake of peptides using MS methods and/or imaging studies (e.g., fluorescence correlation microscopy) to correlate with cell-based functional assays as well as non-cell-based surrogate models 261–265  
Table 1.3

Macrocylic peptide technology platforms highlighting varying macrocylization chemistries

CompanyChemistry platformR&D pipeline (internal/external)
Aileron therapeutics Stapled helical peptides (multiple chemistries) Dual MDM2/MDMX antagonist ALRN-6924 (phase 1/phase 2); GHRH agonist ALRN-5281 (phase 1) 
Bicycle therapeutics Phage display of bicyclic peptides Undisclosed (preclinical) 
Circle pharma Cell permeable macrocyclic peptides Undisclosed (preclinical); Pfizer collaboration 
Encycle therapeutics Amphoteric cyclization (nacellins) Multiple partnerships (preclinical) 
Ensemble therapeutics DNA-programmed macrocycles IL-17 antagonist (partnered with Novartis) and several pharma collaborations (preclinical) 
Fog pharma Cell permeable mini-proteins Undisclosed (preclinical) 
Pepscan therapeutics Chemical linkage of peptides (CLIPS) into scaffolds HIV fusion inhibitor (partnered with Crucell); IBD peptide (partnered with Zealand) 
PeptiDream RNA display of modified cyclic peptides Flu inhibitor (partnered with TMI); Merck & Co and several other pharma collaborations (preclinical) 
Polyphor MacoFinder/PEMfinder platforms for macrocycles and cyclic peptides CXCR4 antagonist POL6326 (phase 2); Antibiotic POL7080 (phase 2); Gilead collaboration (preclinical) 
Protagonist therapeutics Disulfide-rich peptides (DRPs) α4β7 antagonist (preclinical); IL-6 antagonist (partnered with Ironwood Pharmaceuticals) 
Ra Pharmaceuticals RNA display of modified cyclic peptides (Cyclomimetics™) Complement C5 inhibitor RA101495 (phase 1); Merck & Co. partnership (preclinical) 
CompanyChemistry platformR&D pipeline (internal/external)
Aileron therapeutics Stapled helical peptides (multiple chemistries) Dual MDM2/MDMX antagonist ALRN-6924 (phase 1/phase 2); GHRH agonist ALRN-5281 (phase 1) 
Bicycle therapeutics Phage display of bicyclic peptides Undisclosed (preclinical) 
Circle pharma Cell permeable macrocyclic peptides Undisclosed (preclinical); Pfizer collaboration 
Encycle therapeutics Amphoteric cyclization (nacellins) Multiple partnerships (preclinical) 
Ensemble therapeutics DNA-programmed macrocycles IL-17 antagonist (partnered with Novartis) and several pharma collaborations (preclinical) 
Fog pharma Cell permeable mini-proteins Undisclosed (preclinical) 
Pepscan therapeutics Chemical linkage of peptides (CLIPS) into scaffolds HIV fusion inhibitor (partnered with Crucell); IBD peptide (partnered with Zealand) 
PeptiDream RNA display of modified cyclic peptides Flu inhibitor (partnered with TMI); Merck & Co and several other pharma collaborations (preclinical) 
Polyphor MacoFinder/PEMfinder platforms for macrocycles and cyclic peptides CXCR4 antagonist POL6326 (phase 2); Antibiotic POL7080 (phase 2); Gilead collaboration (preclinical) 
Protagonist therapeutics Disulfide-rich peptides (DRPs) α4β7 antagonist (preclinical); IL-6 antagonist (partnered with Ironwood Pharmaceuticals) 
Ra Pharmaceuticals RNA display of modified cyclic peptides (Cyclomimetics™) Complement C5 inhibitor RA101495 (phase 1); Merck & Co. partnership (preclinical) 

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