Pharmacologic characterization of atogepant: A potent and selective calcitonin gene-related peptide receptor antagonist.

BACKGROUND: The trigeminal sensory neuropeptide calcitonin gene-related peptide (CGRP) is identified as an essential element in migraine pathogenesis. METHODS: In vitro and in vivo studies evaluated pharmacologic properties of the CGRP receptor antagonist atogepant. Radioligand binding using 125I-CGRP and cyclic adenosine monophosphate (cAMP) accumulation assays were conducted in human embryonic kidney 293 cells to assess affinity, functional potency and selectivity. Atogepant in vivo potency was assessed in the rat nitroglycerine model of facial allodynia and primate capsaicin-induced dermal vasodilation (CIDV) pharmacodynamic model. Cerebrospinal fluid/brain penetration and behavioral effects of chronic dosing and upon withdrawal were evaluated in rats. RESULTS: Atogepant exhibited high human CGRP receptor-binding affinity and potently inhibited human α-CGRP-stimulated cAMP responses. Atogepant exhibited significant affinity for the amylin1 receptor but lacked appreciable affinities for adrenomedullin, calcitonin and other known neurotransmitter receptor targets. Atogepant dose-dependently inhibited facial allodynia in the rat nitroglycerine model and produced significant CIDV inhibition in primates. Brain penetration and behavioral/physical signs during chronic dosing and abrupt withdrawal were minimal in rats. CONCLUSIONS: Atogepant is a competitive antagonist with high affinity, potency and selectivity for the human CGRP receptor. Atogepant demonstrated a potent, concentration-dependent exposure/efficacy relationship between atogepant plasma concentrations and inhibition of CGRP-dependent effects.

Deep Learning Applied to Raman Spectroscopy for the Detection of Microsatellite Instability/MMR Deficient Colorectal Cancer.

Defective DNA mismatch repair is one pathogenic pathway to colorectal cancer. It is characterised by microsatellite instability which provides a molecular biomarker for its detection. Clinical guidelines for universal testing of this biomarker are not met due to resource limitations; thus, there is interest in developing novel methods for its detection. Raman spectroscopy (RS) is an analytical tool able to interrogate the molecular vibrations of a sample to provide a unique biochemical fingerprint. The resulting datasets are complex and high-dimensional, making them an ideal candidate for deep learning, though this may be limited by small sample sizes. This study investigates the potential of using RS to distinguish between normal, microsatellite stable (MSS) and microsatellite unstable (MSI-H) adenocarcinoma in human colorectal samples and whether deep learning provides any benefit to this end over traditional machine learning models. A 1D convolutional neural network (CNN) was developed to discriminate between healthy, MSI-H and MSS in human tissue and compared to a principal component analysis-linear discriminant analysis (PCA-LDA) and a support vector machine (SVM) model. A nested cross-validation strategy was used to train 30 samples, 10 from each group, with a total of 1490 Raman spectra. The CNN achieved a sensitivity and specificity of 83% and 45% compared to PCA-LDA, which achieved a sensitivity and specificity of 82% and 51%, respectively. These are competitive with existing guidelines, despite the low sample size, speaking to the molecular discriminative power of RS combined with deep learning. A number of biochemical antecedents responsible for this discrimination are also explored, with Raman peaks associated with nucleic acids and collagen being implicated.

Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature.

Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.

Raman spectroscopy to differentiate between fresh tissue samples of glioma and normal brain: a comparison with 5-ALA-induced fluorescence-guided surgery.

OBJECTIVE: Raman spectroscopy is a biophotonic tool that can be used to differentiate between different tissue types. It is nondestructive and no sample preparation is required. The aim of this study was to evaluate the ability of Raman spectroscopy to differentiate between glioma and normal brain when using fresh biopsy samples and, in the case of glioblastomas, to compare the performance of Raman spectroscopy to predict the presence or absence of tumor with that of 5-aminolevulinic acid (5-ALA)-induced fluorescence. METHODS: A principal component analysis (PCA)-fed linear discriminant analysis (LDA) machine learning predictive model was built using Raman spectra, acquired ex vivo, from fresh tissue samples of 62 patients with glioma and 11 glioma-free brain samples from individuals undergoing temporal lobectomy for epilepsy. This model was then used to classify Raman spectra from fresh biopsies from resection cavities after functional guided, supramaximal glioma resection. In cases of glioblastoma, 5-ALA-induced fluorescence at the resection cavity biopsy site was recorded, and this was compared with the Raman spectral model prediction for the presence of tumor. RESULTS: The PCA-LDA predictive model demonstrated 0.96 sensitivity, 0.99 specificity, and 0.99 accuracy for differentiating tumor from normal brain. Twenty-three resection cavity biopsies were taken from 8 patients after supramaximal resection (6 glioblastomas, 2 oligodendrogliomas). Raman spectroscopy showed 1.00 sensitivity, 1.00 specificity, and 1.00 accuracy for predicting tumor versus normal brain in these samples. In the glioblastoma cases, where 5-ALA-induced fluorescence was used, the performance of Raman spectroscopy was significantly better than the predictive value of 5-ALA-induced fluorescence, which showed 0.07 sensitivity, 1.00 specificity, and 0.24 accuracy (p = 0.0009). CONCLUSIONS: Raman spectroscopy can accurately classify fresh tissue samples into tumor versus normal brain and is superior to 5-ALA-induced fluorescence. Raman spectroscopy could become an important intraoperative tool used in conjunction with 5-ALA-induced fluorescence to guide extent of resection in glioma surgery.

Identification of a novel RAMP-independent CGRP receptor antagonist.

Identification of an HIV integrase inhibitor with micromolar affinity for the CGRP receptor led to the discovery of a series of structurally novel CGRP receptor antagonists. Optimization of this series produced compound 16, a low-molecular weight CGRP receptor antagonist with good pharmacokinetic properties in both rat and dog. In contrast to other nonpeptide antagonists, the activity of 16 was affected by the presence of divalent cations and showed evidence of an alternative, RAMP-independent CGRP receptor binding site.

Pharmacological properties of MK-3207, a potent and orally active calcitonin gene-related peptide receptor antagonist.

Calcitonin gene-related peptide (CGRP) has long been hypothesized to play a key role in migraine pathophysiology, and the advent of small-molecule antagonists has clearly demonstrated a clinical link between blocking the CGRP receptor and migraine efficacy. 2-[(8R)-8-(3,5-Difluorophenyl)-10-oxo-6,9-diazaspiro[4.5]dec-9-yl]-N-[(2R)-2'-oxo-1,1',2',3-tetrahydrospiro[indene-2,3'-pyrrolo[2,3-b]pyridin]-5-yl]acetamide (MK-3207) represents the third CGRP receptor antagonist to display clinical efficacy in migraine trials. Here, we report the pharmacological characterization of MK-3207, a potent and orally bioavailable CGRP receptor antagonist. In vitro, MK-3207 is a potent antagonist of the human and rhesus monkey CGRP receptors (K(i) = 0.024 nM). In common with other CGRP receptor antagonists, MK-3207 displays lower affinity for CGRP receptors from other species, including canine and rodent. As a consequence of species selectivity, the in vivo potency was assessed in a rhesus monkey pharmacodynamic assay measuring capsaicin-induced changes in forearm dermal blood flow via laser Doppler imaging. MK-3207 produced a concentration-dependent inhibition of dermal vasodilation, with plasma concentrations of 0.8 and 7 nM required to block 50 and 90% of the blood flow increase, respectively. The tritiated analog [3H]MK-3207 was used to study the binding characteristics on the human CGRP receptor. [3H]MK-3207 displayed reversible and saturable binding (K(D) = 0.06 nM), and the off-rate was determined to be 0.012 min(-1), with a t(1/2) value of 59 min. In vitro autoradiography studies on rhesus monkey brain slices identified the highest level of binding in the cerebellum, brainstem, and meninges. Finally, as an index of central nervous system penetrability, the in vivo cerebrospinal fluid/plasma ratio was determined to be 2 to 3% in cisterna magna-ported rhesus monkeys.

Role of hERG potassium channel assays in drug development.

Numerous structurally and functionally unrelated drugs block the hERG potassium channel. HERG channels are involved in cardiac action potential repolarization, and reduced function of hERG lengthens ventricular action potentials, prolongs the QT interval in an electrocardiogram, and increases the risk for potentially fatal ventricular arrhythmias. In order to reduce the risk of investing resources in a drug candidate that fails preclinical safety studies because of QT prolongation, it is important to screen compounds for activity on hERG channels early in the lead optimization process. A number of hERG assays are available, ranging from high throughput binding assays on stably expressed recombinant channels to very time consuming electrophysiological examinations in cardiac myocytes. Depending on the number of compounds to be tested, binding assays or functional assays measuring membrane potential or Rb(+) flux, combined with electrophysiology on a few compounds, can be used to efficiently develop the structure-function relationship of hERG interactions.

The impact of I(Kr) blockade on medicinal chemistry programs.

Inhibition of the cardiac I(Kr) current leads to prolongation of the QT interval and to a risk of ventricular arrhythmia. This activity has been observed for a wide range of small molecules and results from their binding to the hERG ion channel. The off-target inhibition of I(Kr) presents a daunting challenge for many medicinal chemistry programs. This review article presents case studies that describe a rang of findings across several projects at Merck. The article begins with a review of findings from the original efforts to identify I(Kr) blockers as antiarrhythmic therapeutics. A discussion follows of in vitro and in vivo assays that have been utilized for the assessment of I(Kr) inhibition. General SAR rules that have been found to be useful guides for diminishing hERG activity in lead compounds are discussed and case studies are presented that illustrate specific observations. The case studies highlight how the issue of hERG antagonism was navigated on four distinct medicinal chemistry programs.

Benzodiazepine calcitonin gene-related peptide (CGRP) receptor antagonists: optimization of the 4-substituted piperidine.

In our continuing effort to identify CGRP receptor antagonists for the acute treatment of migraine, we have undertaken a study to evaluate alternative 4-substituted piperidines to the lead dihydroquinazolinone 1. In this regard, we have identified the piperidinyl-azabenzimidazolone and phenylimidazolinone structures which, when incorporated into the benzodiazepine core, afford potent CGRP receptor antagonists (e.g., 18 and 29). These studies produced a potent analog (18) which overcomes the instability issues associated with the lead structure 1. A general pharmacophore for the 4-substituted piperidine component of these CGRP receptor antagonists is also presented.

Biochemical and structural characterization of a novel class of inhibitors of the type 1 insulin-like growth factor and insulin receptor kinases.

The type 1 insulin-like growth factor receptor (IGF-1R) is often overexpressed on tumor cells and is believed to play an important role in anchorage-independent proliferation. Additionally, cell culture studies have indicated that IGF-1R confers increased resistance to apoptosis caused by radiation or chemotherapeutic agents. Thus, inhibitors of the intracellular kinase domain of this receptor may have utility for the clinical treatment of cancer. As part of an effort to develop clinically useful inhibitors of IGF-1R kinase, a novel class of pyrrole-5-carboxaldehyde compounds was investigated. The compounds exhibited selectivity against the closely related insulin receptor kinase intrinsically and in cell-based assays. The inhibitors formed a reversible, covalent adduct at the kinase active site, and treatment of such adducts with sodium borohydride irreversibly inactivated the enzyme. Analysis of a tryptic digest of a covalently modified IGF-1R kinase fragment revealed that the active site Lys1003 had been reductively alkylated with the aldehyde inhibitor. Reductive alkylation of the insulin receptor kinase with one of these inhibitors led to a similarly inactivated enzyme which was examined by X-ray crystallography. The crystal structure confirmed the modification of the active site lysine side chain and revealed details of the key interactions between the inhibitor and enzyme.

Inhibitors of farnesyltransferase and geranylgeranyltransferase-I for antitumor therapy: substrate-based design, conformational constraint and biological activity.

The development of farnesyltransferase inhibitors, a novel approach to non-cytotoxic anticancer therapy, has been an active area of research over the past decade. Compounds that have advanced to clinical trials were evolved both from substrate-based design efforts and from compound library screening hits. This review focuses on the effort at Merck to evolve inhibitors from the protein substrate of farnesyltransferase, which resulted in the identification of a non-peptide inhibitor for clinical evaluation. X-ray crystal structures of farnesyltransferase complexed with early peptidomimetic as well as later non-peptide inhibitors have validated this design approach. NMR spectroscopic methods for studying enzyme-bound inhibitor structure, in conjunction with the use of conformational constraints, were critical components of subsequent efforts to provide potent inhibitors with varying levels of farnesyltransferase and geranylgeranyltransferase-I inhibitory specificity. Several of these compounds were important tools for investigating the use of prenyltransferase inhibitors to target Ki-Ras-mediated tumor growth.

3-Aminopyrrolidinone farnesyltransferase inhibitors: design of macrocyclic compounds with improved pharmacokinetics and excellent cell potency.

A series of macrocyclic 3-aminopyrrolidinone farnesyltransferase inhibitors (FTIs) has been synthesized. Compared with previously described linear 3-aminopyrrolidinone FTIs such as compound 1, macrocycles such as 49 combined improved pharmacokinetic properties with a reduced potential for side effects. In dogs, oral bioavailability was good to excellent, and increases in plasma half-life were due to attenuated clearance. It was observed that in vivo clearance correlated with the flexibility of the molecules and this concept proved useful in the design of FTIs that exhibited low clearance, such as FTI 78. X-ray crystal structures of compounds 49 and 66 complexed with farnesyltransferase (FTase)-farnesyl diphosphate (FPP) were determined, and they provide details of the key interactions in such ternary complexes. Optimization of this 3-aminopyrrolidinone series of compounds led to significant increases in potency, providing 83 and 85, the most potent inhibitors of FTase in cells described to date.