Predicting human half-life for insulin analogs: An inter-drug approach.

An inter-drug approach, applying pharmacokinetic information for insulin analogs in different animal species, rat, dog and pig, performed better compared to allometric scaling for human translation of intra-venous half-life and only required data from a single animal species for reliable predictions. Average fold error (AFE) between 1.2-1.7 were determined for all species and for multispecies allometric scaling AFE was 1.9. A slightly larger prediction error for human half-life was determined from in vitro human insulin receptor affinity data (AFE on 2.3-2.6). The requirements for the inter-drug approach were shown to be a span of at least 2 orders of magnitude in half-life for the included drugs and a shared clearance mechanism. The insulin analogs in this study were the five fatty acid protracted analogs: Insulin degludec, insulin icodec, insulin 320, insulin 338 and insulin 362, as well as the non-acylated analog insulin aspart.

Molecular Representations in Machine-Learning-Based Prediction of PK Parameters for Insulin Analogs.

Therapeutic peptides and proteins derived from either endogenous hormones, such as insulin, or de novo design via display technologies occupy a distinct pharmaceutical space in between small molecules and large proteins such as antibodies. Optimizing the pharmacokinetic (PK) profile of drug candidates is of high importance when it comes to prioritizing lead candidates, and machine-learning models can provide a relevant tool to accelerate the drug design process. Predicting PK parameters of proteins remains difficult due to the complex factors that influence PK properties; furthermore, the data sets are small compared to the variety of compounds in the protein space. This study describes a novel combination of molecular descriptors for proteins such as insulin analogs, where many contained chemical modifications, e.g., attached small molecules for protraction of the half-life. The underlying data set consisted of 640 structural diverse insulin analogs, of which around half had attached small molecules. Other analogs were conjugated to peptides, amino acid extensions, or fragment crystallizable regions. The PK parameters clearance (CL), half-life (T1/2), and mean residence time (MRT) could be predicted by using classical machine-learning models such as Random Forest (RF) and Artificial Neural Networks (ANN) with root-mean-square errors of CL of 0.60 and 0.68 (log units) and average fold errors of 2.5 and 2.9 for RF and ANN, respectively. Both random and temporal data splittings were employed to evaluate ideal and prospective model performance with the best models, regardless of data splitting, achieving a minimum of 70% of predictions within a twofold error. The tested molecular representations include (1) global physiochemical descriptors combined with descriptors encoding the amino acid composition of the insulin analogs, (2) physiochemical descriptors of the attached small molecule, (3) protein language model (evolutionary scale modeling) embedding of the amino acid sequence of the molecules, and (4) a natural language processing inspired embedding (mol2vec) of the attached small molecule. Encoding the attached small molecule via (2) or (4) significantly improved the predictions, while the benefit of using the protein language model-based encoding (3) depended on the used machine-learning model. The most important molecular descriptors were identified as descriptors related to the molecular size of both the protein and protraction part using Shapley additive explanations values. Overall, the results show that combining representations of proteins and small molecules was key for PK predictions of insulin analogs.

Molecular Engineering of Insulin Icodec, the First Acylated Insulin Analog for Once-Weekly Administration in Humans.

Here, we describe the molecular engineering of insulin icodec to achieve a plasma half-life of 196 h in humans, suitable for once-weekly subcutaneously administration. Insulin icodec is based on re-engineering of the ultra-long oral basal insulin OI338 with a plasma half-life of 70 h in humans. This systematic re-engineering was accomplished by (1) further increasing the albumin binding by changing the fatty diacid from a 1,18-octadecanedioic acid (C18) to a 1,20-icosanedioic acid (C20) and (2) further reducing the insulin receptor affinity by the B16Tyr → His substitution. Insulin icodec was selected by screening for long intravenous plasma half-life in dogs while ensuring glucose-lowering potency following subcutaneous administration in rats. The ensuing structure-activity relationship resulted in insulin icodec. In phase-2 clinical trial, once-weekly insulin icodec provided safe and efficacious glycemic control comparable to once-daily insulin glargine in type 2 diabetes patients. The structure-activity relationship study leading to insulin icodec is presented here.

Engineering of Orally Available, Ultralong-Acting Insulin Analogues: Discovery of OI338 and OI320.

Recently, the first basal oral insulin (OI338) was shown to provide similar treatment outcomes to insulin glargine in a phase 2a clinical trial. Here, we report the engineering of a novel class of basal oral insulin analogues of which OI338, 10, in this publication, was successfully tested in the phase 2a clinical trial. We found that the introduction of two insulin substitutions, A14E and B25H, was needed to provide increased stability toward proteolysis. Ultralong pharmacokinetic profiles were obtained by attaching an albumin-binding side chain derived from octadecanedioic (C18) or icosanedioic acid (C20) to the lysine in position B29. Crucial for obtaining the ultralong PK profile was also a significant reduction of insulin receptor affinity. Oral bioavailability in dogs indicated that C18-based analogues were superior to C20-based analogues. These studies led to the identification of the two clinical candidates OI338 and OI320 (10 and 24, respectively).

Molecular engineering of safe and efficacious oral basal insulin.

Recently, the clinical proof of concept for the first ultra-long oral insulin was reported, showing efficacy and safety similar to subcutaneously administered insulin glargine. Here, we report the molecular engineering as well as biological and pharmacological properties of these insulin analogues. Molecules were designed to have ultra-long pharmacokinetic profile to minimize variability in plasma exposure. Elimination plasma half-life of ~20 h in dogs and ~70 h in man is achieved by a strong albumin binding, and by lowering the insulin receptor affinity 500-fold to slow down receptor mediated clearance. These insulin analogues still stimulate efficient glucose disposal in rats, pigs and dogs during constant intravenous infusion and euglycemic clamp conditions. The albumin binding facilitates initial high plasma exposure with a concomitant delay in distribution to peripheral tissues. This slow appearance in the periphery mediates an early transient hepato-centric insulin action and blunts hypoglycaemia in dogs in response to overdosing.

The counterregulatory response to hypoglycaemia in the pig.

The domestic pig is commonly used as animal model in the pharmaceutical development of new therapeutics for treatment of diabetes. Since a formal definition of hypoglycaemia only exists in humans, the purpose of this study was to assess the counterregulatory response in the domestic pig at glucose levels known to induce symptoms of hypoglycaemia in humans. Six pigs were included in hyperinsulinaemic glucose clamps with plasma glucose targets of 2, 3 and 5 mmol/L in a cross-over design, and the associated glucose counterregulatory response was assessed by measuring glucose kinetics and levels of glucagon, c-peptide, catecholamines, cortisol and growth hormone. Results showed that the 2 and 3 vs 5 mmol/L clamps significantly decreased and increased the secretion of c-peptide and glucagon, respectively (P < .05). This finding was associated with increased rate of glucose appearance (Ra ) and decreased rate of glucose disappearance (Rd ) (P < .001). No marked differences in the catecholamine, growth hormone or cortisol response were observed. Consequently, like humans, pigs respond to hypoglycaemia by decreasing the pancreatic output of insulin while increasing that of glucagon, with increased glucose mobilization and decreased glucose disposal as a result. The hypoglycaemic clamps did not result in a marked secretion of the other counterregulatory hormones.

Elucidating the Mechanism of Absorption of Fast-Acting Insulin Aspart: The Role of Niacinamide.

PURPOSE: Fast-acting insulin aspart (faster aspart) is a novel formulation of insulin aspart containing two additional excipients: niacinamide, to increase early absorption, and L-arginine, to optimize stability. The aim of this study was to evaluate the impact of niacinamide on insulin aspart absorption and to investigate the mechanism of action underlying the accelerated absorption. METHODS: The impact of niacinamide was assessed in pharmacokinetic analyses in pigs and humans, small angle X-ray scattering experiments, trans-endothelial transport assays, vascular tension measurements, and subcutaneous blood flow imaging. RESULTS: Niacinamide increased the rate of early insulin aspart absorption in pigs, and pharmacokinetic modelling revealed this effect to be most pronounced up to ~30-40 min after injection in humans. Niacinamide increased the relative monomer fraction of insulin aspart by ~35%, and the apparent permeability of insulin aspart across an endothelial cell barrier by ~27%. Niacinamide also induced a concentration-dependent vasorelaxation of porcine arteries, and increased skin perfusion in pigs. CONCLUSION: Niacinamide mediates the acceleration of initial insulin aspart absorption, and the mechanism of action appears to be multifaceted. Niacinamide increases the initial abundance of insulin aspart monomers and transport of insulin aspart after subcutaneous administration, and also mediates a transient, local vasodilatory effect.

Spatial distribution of soluble insulin in pig subcutaneous tissue: Effect of needle length, injection speed and injected volume.

The spatial distribution of a soluble insulin formulation was visualized and quantified in 3-dimensions using X-ray computed tomography. The drug distribution was visualized for ex vivo injections in pig subcutaneous tissue. Pig subcutaneous tissue has very distinct layers, which could be separated in the tomographic reconstructions and the amount of drug in each tissue class was quantified. With a scan time of about 45min per sample, and a robust segmentation it was possible to analyze differences in the spatial drug distribution between several similar injections. It was studied how the drug distribution was effected by needle length, injection speed and injected volume. For an injected volume of 0.1ml and injection depth of 8mm about 50% of the injections were partly intramuscular. Using a 5mm needle resulted in purely subcutaneous injections with minor differences in the spatial drug distribution between injections. Increasing the injected volume from 0.1ml to 1ml did not increase the intramuscular volume fraction, but gave a significantly higher volume fraction placed in the fascia separating the deep and superficial subcutaneous fat layers. Varying the injection speed from 25l/s up to 300l/s gave no changes in the drug concentration distribution. The method presented gives novel insight into subcutaneous injections of soluble insulin drugs and can be used to optimize the injection technique for subcutaneous drug administration in preclinical studies of rodents.

In silico modelling of permeation enhancement potency in Caco-2 monolayers based on molecular descriptors and random forest.

Structural traits of permeation enhancers are important determinants of their capacity to promote enhanced drug absorption. Therefore, in order to obtain a better understanding of structure-activity relationships for permeation enhancers, a Quantitative Structural Activity Relationship (QSAR) model has been developed. The random forest-QSAR model was based upon Caco-2 data for 41 surfactant-like permeation enhancers from Whitehead et al. (2008) and molecular descriptors calculated from their structure. The QSAR model was validated by two test-sets: (i) an eleven compound experimental set with Caco-2 data and (ii) nine compounds with Caco-2 data from literature. Feature contributions, a recent developed diagnostic tool, was applied to elucidate the contribution of individual molecular descriptors to the predicted potency. Feature contributions provided easy interpretable suggestions of important structural properties for potent permeation enhancers such as segregation of hydrophilic and lipophilic domains. Focusing on surfactant-like properties, it is possible to model the potency of the complex pharmaceutical excipients, permeation enhancers. For the first time, a QSAR model has been developed for permeation enhancement. The model is a valuable in silico approach for both screening of new permeation enhancers and physicochemical optimisation of surfactant enhancer systems.

In silico prediction of cytochrome P450 2D6 and 3A4 inhibition using Gaussian kernel weighted k-nearest neighbor and extended connectivity fingerprints, including structural fragment analysis of inhibitors versus noninhibitors.

Inhibition of cytochrome P450 (CYP) enzymes is unwanted because of the risk of severe side effects due to drug-drug interactions. We present two in silico Gaussian kernel weighted k-nearest neighbor models based on extended connectivity fingerprints that classify CYP2D6 and CYP3A4 inhibition. Data used for modeling consisted of diverse sets of 1153 and 1382 drug candidates tested for CYP2D6 and CYP3A4 inhibition in human liver microsomes. For CYP2D6, 82% of the classified test set compounds were predicted to the correct class. For CYP3A4, 88% of the classified compounds were correctly classified. CYP2D6 and CYP3A4 inhibition were additionally classified for an external test set on 14 drugs, and multidimensional scaling plots showed that the drugs in the external test set were in the periphery of the training sets. Furthermore, fragment analyses were performed and structural fragments frequent in CYP2D6 and CYP3A4 inhibitors and noninhibitors are presented.

Increase of neuronal histamine in obese rats is associated with decreases in body weight and plasma triglycerides.

OBJECTIVE: The purpose of the present study was to examine the metabolic effects of a specific histamine H(3) receptor antagonist, the cinnamic amide NNC 0038-0000-1202 (NNC 38-1202). RESEARCH METHODS AND PROCEDURES: Effects of NNC 38-1202 on paraventricular levels of histamine and acute effects on food intake were followed in normal rats, whereas effects on body weight homeostasis and lipid metabolism were studied in a rat model of diet-induced obesity (DIO). RESULTS: NNC 38-1202, administered as single oral doses of 15 and 30 mg/kg, significantly (p < 0.01) increased paraventricular histamine by 339 +/- 54% and 403 +/- 105%, respectively, compared with basal levels. The same doses produced significant (p < 0.01) reductions in food intake. In DIO rats receiving NNC 38-1202 in a daily dose of 5 mg/kg for 22 days, a decrease in food intake was associated with a significant (p < 0.001) net loss of body weight (-11.0 +/- 4.8 grams), compared with rats receiving vehicle, which gained 13.6 +/- 3.0 grams. Also, NNC 38-1202 significantly (p < 0.05) reduced plasma triglycerides by approximately 42%, in parallel with increases in plasma free fatty acids and beta-hydroxybutyrate levels. Despite reductions in food intake and body weight following administration of NNC 38-1202, no sign of a decrease in energy expenditure was observed, and whole-body lipid oxidation was significantly (p < 0.05) increased in the period after dosing. DISCUSSION: The present study suggests that antagonistic targeting of the histamine H(3) receptor decreases food intake, body weight, and plasma TG levels and, thus, represents an interesting approach to treatment of obesity and associated hyperlipidemia.

Oxidation of bovine serum albumin initiated by the Fenton reaction--effect of EDTA, tert-butylhydroperoxide and tetrahydrofuran.

Oxidation of bovine serum albumin (BSA) was investigated using different oxidants: The water-soluble azo-initiator 2,2'azo-bis-(2-amidinopropane) hydrochloride (AAPH), a combination of FeCl(3) and ascorbate or the Fenton oxidant consisting of FeCl(2), H(2)O(2) and EDTA. In addition, the effects of exogenous compounds such as tert-butyl hydroperoxide (tBuOOH) or solvents such as tetrahydrofuran (THF), often used in model systems, was evaluated. The extent of protein damage was studied by measuring protein carbonyl groups and protein hydroperoxides. The interaction between Fenton oxidant and EDTA, THF or tBuOOH was further characterized using spin trapping electron spin resonance (ESR) spectroscopy. The results showed that the extent of protein oxidation depended on the oxidant used. The Fenton oxidant was the most reactive of the initiators tested. However, in the absence of EDTA, the Fenton system produced protein carbonyl groups on BSA equivalent to that obtained with the other oxidants, however, significantly more protein hydroperoxide was produced. Surprisingly, it was also found that addition of tBuOOH or THF to BSA reduced protein damage when the oxidation was initiated with the Fenton oxidant. ESR investigation showed that EDTA played a key role in the generation of free radicals. It was also revealed that in an EDTA containing system both tBuOOH and THF were able to react with radicals without inducing protein damage in effect protecting BSA from oxidative damage.

Evaluation of activity of selected antioxidants on proteins in solution and in emulsions.

Protection against protein oxidation by lipophilic and hydrophilic antioxidants in model systems using bovine serum albumin (BSA) in solution alone, or in an emulsion with linolenic acid methyl ester (LnMe) was found to be strongly dependent on the oxidation initiator. Tocopherol, Trolox, or the carotenoids astaxanthin and canthaxanthin were incubated with BSA or BSA/LnMe and oxidation was initiated either with the water-soluble azo-initiator 2,2' azo-bis-(2-amidinopropane) hydrochloride (AAPH), or FeCl3 and ascorbate, or the Fenton system using FeCl2/EDTA/H2O2, or with the singlet oxygen generating species anthracene-9,10-dipropionic acid disodium 1,4 endoperoxide (NDPO2). The results show that all the antioxidants tested were inefficient in the system with FeCl3/ascorbate. However, with the other initiating agents, the hydrophilic antioxidant, Trolox, was the most effective in preventing both protein and lipid oxidation. In contrast the lipophilic antioxidants were ineffective in preventing oxidation of BSA in aqueous solution, but did show some moderate antioxidative activity on protein and lipid in the BSA/LnMe system. Using the singlet oxygen generating system it was also demonstrated that Trolox always provided better protection of the protein than tocopherol and the carotenoids in both the BSA and the BSA/LnMe systems. In conclusion, prevention of protein oxidation using a water-soluble antioxidant has a protective effect on the lipid fraction and this approach deserves further attention in complex biological systems.

In silico prediction of membrane permeability from calculated molecular parameters.

A data set consisting of 712 compounds was used for classification into two classes with respect to membrane permeation in a cell-based assay: (0) apparent permeability (P(app)) below 4 x 10(-6) cm/s and (1) P(app) on 4 x 10(-6) cm/s or higher. Nine molecular descriptors were calculated for each compound and Nearest-Neighbor classification was applied using five neighbors as optimized by full cross-validation. A model based on five descriptors, number of flex bonds, number of hydrogen bond acceptors and donors, and molecular and polar surface area, was selected by variable selection. In an external test set of 112 compounds, 104 compounds were classified and 8 compounds were judged as "unknown". Among the 104 compounds, 16 were misclassified corresponding to a misclassification rate of 15% and no compounds were falsely predicted in the nonpermeable class.

Flavor release measurement by atmospheric pressure chemical ionization ion trap mass spectrometry, construction of interface and mathematical modeling of release profiles.

An instrumental on-line retronasal flavor analysis was developed to obtain information about the release of flavor compounds in expired air from humans during eating. The volatile flavor compounds were measured by ion trap mass spectrometry with an atmospheric pressure chemical ionization source (APCI). An interface was designed to sample the breath directly from the nose. The repeatability in vitro for seven different flavor compounds came out with relative standard derivation less than 10% in most cases, which is acceptable. In vitro quantification was carried out by a determination of the concentration in the gas phase over a flavor solution by GC/MS, followed by measurements of intensities by the APCI ion trap. Ion suppression by acetone in the breath was negligible at concentration levels relevant in these experiments. The instrumental limits of detection for menthone and menthol coincide with that of the flavor detection threshold. An application study on the release of menthone and menthol from chewing gum by a group of six test persons was performed. Flavored chewing gum was used as a model matrix because of the long chewing periods and the simplicity of the system. It is concluded that the interface and the method can be used to measure breath from the nose. A mathematical model of the data was developed to give a quantitative method for description and characterization of the release of flavor compounds. The release profiles consisted of two sequences, one for a chewing period, and one for a phasing out process. The proposed method for modeling provided a reasonable description of the release process. In addition to flavor compounds, this new interface and mathematical application could provide information on chemicals in the human breath, which could be interesting, for example, within medical diagnosis.