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Enhancing the imine reductase activity of a promiscuous glucose dehydrogenase for scalable manufacturing of a chiral neprilysin inhibitor precursor
No full textImine reductases (IREDs) have been identified as an important class of biocatalysts to synthesize chiral secondary amines with substantial promise for industrial application. Here, we report the promiscuous imine reductase activity of a glucose dehydrogenase (GDH), representing a new class of highly selective IREDs. Starting from GDH-105, a commercial glucose dehydrogenase variant typically used for NAD(P)H regeneration, eight rounds of directed evolution were used to convert this enzyme into a highly active IRED for the manufacture of a chiral neprilysin inhibitor precursor with excellent chemo- and stereoselectivity, improved NADH cofactor specificity, and high thermal stability. The evolved variant GDH-201 showed excellent productivity of 99% conversion over 3 h at 50 g/L keto substrate concentration and 10% enzyme loading with respect to the keto substrate. Early process development studies at multigram scale provided the product in 94% yield with >99% purity as a single diastereomer
Midostaurin drug interaction profile: a comprehensive assessment of CYP3A, CYP2B6, and CYP2C8 drug substrates, and oral contraceptives in healthy participants.
No full textMidostaurin, approved for treating FLT-3-mutated acute myeloid leukemia and advanced systemic mastocytosis, is metabolized by cytochrome P450 (CYP) 3A4 to two major metabolites, and may inhibit and/or induce CYP3A, CYP2B6, and CYP2C8. Two studies investigated the impact of midostaurin on CYP substrate drugs and oral contraceptives in healthy participants.Using sentinel dosing for participants' safety, the effects of midostaurin at steady state following 25-day (Study 1) or 24-day (Study 2) dosing with 50 mg twice daily were evaluated on CYP substrates, midazolam (CYP3A4), bupropion (CYP2B6), and pioglitazone (CYP2C8) in Study 1; and monophasic oral contraceptives (containing ethinylestradiol [EES] and levonorgestrel [LVG]) in Study 2.In Study 1, midostaurin resulted in a 10% increase in midazolam peak plasma concentrations (Cmax), and 3-4% decrease in total exposures (AUC). Bupropion showed a 55% decrease in Cmax and 48-49% decrease in AUCs. Pioglitazone showed a 10% decrease in Cmax and 6% decrease in AUC. In Study 2, midostaurin resulted in a 26% increase in Cmax and 7-10% increase in AUC of EES; and a 19% increase in Cmax and 29-42% increase in AUC of LVG. Midostaurin 50 mg twice daily for 28 days ensured that steady-state concentrations of midostaurin and the active metabolites were achieved by the time of CYP substrate drugs or oral contraceptive dosing. No safety concerns were reported.Midostaurin neither inhibits nor induces CYP3A4 and CYP2C8, and weakly induces CYP2B6. Midostaurin at steady state has no clinically relevant PK interaction on hormonal contraceptives. All treatments were well tolerated
Concentration-QTcF modeling of Icenticaftor from a randomized, placebo- and positive-controlled thorough QT (TQT) study in healthy participants
No full textBackground: Icenticaftor (QBW251) is a potentiator of the cystic fibrosis transmembrane receptor (CFTR). Based on its mechanism of action, icenticaftor is expected to provide benefits in patients with COPD by restoring mucociliary clearance, which would eventually lead to a reduction of bacterial colonization and related inflammatory cascade.
Objective & Methods: A placebo- and positive-controlled, four-way cross-over TQT study was conducted in 46 healthy participants with the objective to assess the effect of therapeutic (300 mg twice daily for 6 days) and supra-therapeutic (750 mg twice daily for 6 days) oral doses of icenticaftor on ECG parameters, including concentration-QTc analysis. Moxifloxacin (400 mg, oral) was used as a positive control.
Results: In the primary concentration-QTc analysis performed on pooled data from Day 1 and Day 6 (steady state), the estimated population slope was shallow and slightly negative: -0.0012 ms/ng/mL (90%CI: -0.00135 to -0.00109). The effect on the QTcF interval (∆ΔQTcF) was predicted to be −1.33 ms (90% CI: −1.48 to -1.19) at the icenticaftor 300 mg b.i.d. peak concentration (geometric mean (GM) was 1093.5 ng/mL) and −5.52 ms (90%CI: −6.12 to −4.92) at 750 mg b.i.d peak concentration (GM Cmax was 4529.4 ng/mL) indicated a mild shortening effect of icenticaftor on QTcF interval length. The results of the by timepoint analysis indicated least squares (LS) placebo corrected mean ∆∆QTcF across time points ranged from -7.9 (90% CI: -10.11 to -5.78) to 0.1 ms (90% CI: -2.03 to 2.20) (at 1 and 24 hours post-dose both on Day 6) in the 750 mg dose group compared with -3.7 (90% CI: -5.35 to -1.97) to 1.6 ms (90% CI: -0.39 to 3.57) (at 1.5 and 24 hours post-dose both on Day 1) in the 300 mg dose group. Assay sensitivity was demonstrated with moxifloxacin.
Conclusion: The large accumulation of exposures especially the 4.3-fold increase in Cmax observed at the icenticaftor 750 mg b.i.d.compared to Icenticaftor 300 mg b.i.d. (2.3-fold) on Day 6 provided a large concentration range (up to 9540 ng/mL) to evaluate the effect of Icenticaftor on ΔΔQTcF. Based on the concentration QTc analysis, an effect on ΔΔQTc
Design, Synthesis, In Vitro and In Vivo Evaluation of Cereblon Binding Bruton’s Tyrosine Kinase (BTK) Degrader CD79 targeted Antibody-Drug Conjugates
No full textAntibody-drug conjugates (ADCs) are an established modality which allows for targeted delivery of a potent molecule, or payload, to a desired site of action. ADCs, wherein the payload is a targeted protein degrader is an emerging area in the field. Herein we describe our efforts of delivering a Bruton’s tyrosine kinase (BTK) bifunctional degrader 1 via a CD79b mAb where the degrader is linked at the ligase binding portion of the payload via a cleavable linker to the mAb. The resulting CD79b ADCs, 3 and 4, exhibit in vitro degradation and cytotoxicity comparable to 1 and ADC 3 can achieve greater in vivo degradation than 1 with markedly reduced systemic exposure of the payload
Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition.
No full textMost drugs are mainly metabolized by cytochrome P450 (CYP450), which can lead to drug-drug interactions (DDI). Specifically, time-dependent inhibition (TDI) of CYP3A4 isoenzyme has been associated with clinically relevant DDI. To overcome potential DDI issues, high-throughput in vitro assays were established to assess the TDI of CYP3A4 during the discovery and lead optimization phases. However, in silico machine learning models would enable an earlier and larger-scale assessment of TDI potential liabilities. For CYP inhibition, most modeling efforts have focused on highly imbalanced and small data sets. Moreover, assay variability is rarely considered, which is key to understand the model's quality and suitability for decision-making. In this work, machine learning models were built for the prediction of TDI of CYP3A4, evaluated prospectively, and compared to the variability of the experimental assay. Different modeling strategies were investigated to assess their influence on the model's performance. Through multitask learning, additional data sets were leveraged for model building, coming from public databases, in-house CYP-related assays, or other pharmaceutical companies (federated learning). Apart from the numerical prediction of inactivation rates of CYP3A4 TDI, three-class predictions were carried out, giving a negative (inactivation rate kobs 0.025 min-1) output. The final multitask graph neural network model achieved misclassification rates of 8 and 7% for positive and negative TDI, respectively. Importantly, the presented deep learning-based predictions had a similar precision to the reproducibility of in vitro experiments and thus offered great opportunities for drug design, early derisk of DDI potential, and selection of experiments. To facilitate CYP inhibition modeling efforts in the public domain, the developed model was used to annotate ∼16 000 publicly available structures, and a surrogate data set is shared as Supporting Information
Parameterization of Physiologically Based Biopharmaceutics Models: Workshop Summary Report
No full textThis article shares the proceedings from the 29th August 2023 (day 1) of the workshop “Physiologically Based Biopharmaceutics Models (PBBM): Best Practices for Drug Product Quality: Regulatory and Industry Perspectives”. The focus of the day was on model parameterization, regulatory authorities from Canada, the USA, Sweden, Belgium and Norway presented their view on PBBM case studies submitted by industry members of the IQ consortium. The presentations shared key questions raised by regulators during the mock exercise, regarding the PBBM input parameters and their justification. These presentations also shed light on the regulatory assessment process, content and format requirements for future PBBM regulatory submissions. In addition, the day 1 breakout presentations and discussions gave the opportunity to share best practices around key questions faced by scientists when parameterizing PBBMs. Key questions included measurement and integration of drug substance solubility for crystalline vs. amorphous drugs; impact of excipients on apparent drug solubility / supersaturation; modeling of acid-base reactions at the surface of the dissolving drug; choice of dissolution methods according to the formulation and drug properties with a view to prediction of the in vivo performance; mechanistic modeling of in vitro product dissolution data to predict in vivo dissolution for various patient populations / species; best practices for characterization of drug precipitation from simple or complex formulations and integration of the data in a PBBM; incorporation of drug permeability into PBBM for various routes of uptake and prediction of permeability along the GI tract
Using pathway-specific polygenic risk scores to investigate disease mechanisms, biomarkers, and treatment response
No full textBackground and Hypothesis: Part of the phenotypic heterogeneity across diseases, such as those in the neuroscience and cardiovascular fields, may be due to genetic heterogeneity. These diseases are not solely elicited by single genes, but instead are polygenic with multiple genetic variants having small effects that combine to contribute to disease. Furthermore, within each disease, there are typically several different biological pathways that can impact onset and progression, and each may carry various biomarkers. The study of complex diseases may thus benefit from the use of polygenic approaches specifically focused on relevant biological pathways to examine utility in disease prediction and biomarker selection. One approach that can function towards this goal is the use of a polygenic risk score (PRS). The creation of a PRS can be curated to select SNPs based on biological information relevant to disease. We hypothesize that biologically informed PRSs will be associated with both disease onset and progression in addition to relevant biomarkers that are measurable in clinical trials.
Aims: Genetic evidence for a particular target increases the likelihood that the target will succeed in clinical trials (Minikel et al., 2024). By investigating evidence of the PRS’s association with both disease status and relevant biomarkers, we would enhance our understanding of disease heterogeneity. Additionally, if a pathway-specific PRS shows association with particular measurable biomarkers, it would be suggested that those biomarkers could inform future clinical trials. Overall, the primary aim for our study is to use pathway-specific PRS analyses to assess patient heterogeneity and markers of disease subgroups.
Analysis Design: We propose to develop and calculate pathway-specific PRSs based on biological pathways of interest for several disease areas such as the neuroscience and cardiovascular fields, among others. Upon calculation of the pathway-specific PRSs using summary statistics for our curated list of SNPs, we will test association with diseases and traits in FinnGen, along with relevant biomarkers. If we identify significant biomarkers associated with any of the PRSs, we will perform follow-up analyses including mendelian randomization (MR) and genetic correlations to determine if the results are complimentary to what we observe for the PRS.
Outcome: Our planned analysis fits to the overall FinnGen Scientific Plan as it promotes the use of genetic methodology to add support to the understanding of the mechanisms behind various heterogeneous diseases that can be targeted with novel medications
Interview on Lab2Lab with Ingo Muckenschnabel at Novartis
No full textAt Novartis, a special sample transport system is used at the Basel and Cambridge sites, which pneumatically transports samples provided by various laboratories to the respective analysis device. But the technology goes far beyond a mere transport system. The system, called Lab2Lab, "knows" which analysis device the sample needs to go to and which analysis device is currently occupied. It scans and documents sample data as well as the process. After the measurement in the analysis device is completed, it picks up the sample from the analysis device again and transports it to the next station
M2ara: unraveling metabolomic drug responses in whole-cell MALDI mass spectrometry bioassays.
No full textFast computational evaluation and classification of concentration responses for hundreds of metabolites represented by their mass-to-charge (m/z) ratios is indispensable for unraveling complex metabolomic drug actions in label-free, whole-cell Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI MS) bioassays. In particular, the identification of novel pharmacodynamic biomarkers to determine target engagement, potency, and potential polypharmacology of drug-like compounds in high-throughput applications requires robust data interpretation pipelines. Given the large number of mass features in cell-based MALDI MS bioassays, reliable identification of true biological response patterns and their differentiation from any measurement artefacts that may be present is critical. To facilitate the exploration of metabolomic responses in complex MALDI MS datasets, we present a novel software tool, M2ara. Implemented as a user-friendly R-based shiny application, it enables rapid evaluation of Molecular High Content Screening (MHCS) assay data. Furthermore, we introduce the concept of Curve Response Score (CRS) and CRS fingerprints to enable rapid visual inspection and ranking of mass features. In addition, these CRS fingerprints allow direct comparison of cellular effects among different compounds. Beyond cellular assays, our computational framework can also be applied to MALDI MS-based (cell-free) biochemical assays in general.The software tool, code, and examples are available at https://github.com/CeMOS-Mannheim/M2ara and https://dx.doi.org/10.6084/m9.figshare.25736541
Beyond MABEL: An Integrative Approach to First in Human Dose Selection of Immunomodulators by the Health and Environmental Sciences Institute (HESI) Immuno-Safety Technical Committee (ITC).
No full textAdministration of a new drug candidate in a first-in-human (FIH) clinical trial is a particularly challenging phase in drug development and is especially true for immunomodulators, which are a diverse and complex class of drugs with a broad range of mechanisms of action and associated safety risks. Risk is generally greater for immunostimulators, in which safety concerns are associated with acute toxicity, compared to immunosuppressors, where the risks are related to chronic effects. Current methodologies for FIH dose selection for immunostimulators are focused primarily on identifying the minimum anticipated biological effect level (MABEL), which has often resulted in sub-therapeutic doses, leading to long and costly escalation phases. The Health and Environmental Sciences Institute (HESI) - Immuno-Safety Technical Committee (ITC) organized a project to address this issue through two complementary approaches: (i) an industry survey on FIH dose selection strategies and (ii) detailed case studies for immunomodulators in oncology and non-oncology indications. Key messages from the industry survey responses highlighted a preference toward more dynamic PK/PD approaches as in vitro assays are seemingly not representative of true physiological conditions for immunomodulators. These principles are highlighted in case studies. To address the above themes, we have proposed a revised decision tree, which expands on the guidance by the IQ MABEL Working Group (Leach et al. 2021). This approach facilitates a more refined recommendation of FIH dose selection for immunomodulators, allowing for a nuanced consideration of their mechanisms of action (MOAs) and the associated risk-to-benefit ratio, among other factors