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INDUSTRIAL CASE STUDY ON APPLICATION OF ROLLER COMPACTION MODELLING FOR SCALE UP AND TECHNOLOGY TRANSFER
No full textDry granulation of pharmaceutical powders by the roller compaction process is gaining increasing popularity in the pharmaceutical industry for its simplicity, cost, potential stability benefits and continuous processing options. The Johanson model for dry granulation process by roller compaction is well established in the scientific literature. Its applicability and limitations have been demonstrated. In this work we focus on the application of the model in an industrial environment and highlights how the application of roller compaction modelling in combination with experimental data can guide the successful scale up and equipment transfer. This work is focused on how the model can inform the DoE design and decision-making process to set parameters and choose operating ranges.
In this work, the pilot phase data (Bepex with roll width 30 cm without vacuum de-aeration) was used to model the roller compaction force range on a commercial scale (Bepex with roll width 50 cm with vacuum de-aeration) using the relationship between Pmax and porosity. The predictions helped to establish a good roller compaction process parameter range with wider range for roller compaction force and roll gap, which enabled the production of tablets without work hardening effects and good tablet quality, meeting all the in-process control specifications resulting in free-flowing and compactible granules. These granules ensure that there are no tablet defects such as capping, lamination and sticking. This work demonstrates potential applicability of mechanistic modelling using Johanson model for scale-up from lab scale to commercial roller compaction process settings
Biophysical and structural studies of the ligand-induced interaction of multiple SALL4 zinc finger domains with cereblon
No full textAlthough several proteins are known to interact with cereblon in the presence of molecular glues (MGs), the molecular basis of selectivity is emerging. An understanding of these interactions is important when designing drugs that selectively degrade a given target while sparing others. SALL4 degradation is undesirable and X-ray crystallography and biophysical binding studies expand our understanding of the minimal binding domain recognized by cereblon in the presence of IMiDs pomalidomide and CC-220. Structures of a complex of DDB1:CRBN in the presence of these IMiDs with SALL4 zinc finger domains reveal a novel binding mode involving two adjacent zinc fingers- a primary and a secondary zinc finger. The primary interactions occur between the C-terminal domain IMiD-binding domain of cereblon and SALL4 ZF2, which contains the canonical CXXCG motif that is known to interact with cereblon:pomalidomide complexes. In addition, adjacent SALL4 ZF1 binds to the N-terminal domain of cereblon in the “closed” form. The binding affinities of the single and double zinc finger domains of cereblon are consistent with these structural elements. Adding C-terminal residues to ZF2 results in increased potency due to additional contacts with cereblon. With respect to selectivity, the binding of ZF2 or ZF1-2 domains to the CRBN:CC-220 complex is weaker than the corresponding pomalidomide complexes and structural studies suggest subtle structural changes in cereblon and SALL4 ZF1 that do not occur in the corresponding pomalidomide. These studies further our understanding of the molecular glue-mediated interactions of zinc finger-based proteins with cereblon and may provide structural tools for the prospective design of compounds with reduced binding and degradation of SALL4
Prediction of Small-Molecule Developability Using Large-Scale In Silico ADMET Models.
No full textEarly in silico assessment of the potential of a series of compounds to deliver a drug is one of the major challenges in computer-assisted drug design. The goal is to identify the right chemical series of compounds out of a large chemical space to then subsequently prioritize the molecules with the highest potential to become a drug. Although multiple approaches to assess compounds have been developed over decades, the quality of these predictors is often not good enough and compounds that agree with the respective estimates are not necessarily druglike. Here, we report a novel deep learning approach that leverages large-scale predictions of ∼100 ADMET assays to assess the potential of a compound to become a relevant drug candidate. The resulting score, which we termed bPK score, substantially outperforms previous approaches and showed strong discriminative performance on data sets where previous approaches did not
Sabatolimab (MBG453) model-informed drug development for dose selection in patients with myelodysplastic syndrome/acute myeloid leukemia and solid tumors.
No full textSabatolimab is a novel immunotherapy with immuno-myeloid activity that targets T-cell immunoglobulin domain and mucin domain-3 (TIM-3) on immune cells and leukemic blasts. It is being evaluated for the treatment of myeloid malignancies in the STIMULUS clinical trial program. The objective of this analysis was to support the sabatolimab dose-regimen selection in hematologic malignancies. A population pharmacokinetic (PopPK) model was fit to patients with solid tumors and hematologic malignancies, which included acute myeloid leukemia, myelodysplastic syndrome (including intermediate-, high-, and very high-risk per Revised International Prognostic Scoring System), and chronic myelomonocytic leukemia. The PopPK model, together with a predictive model of sabatolimab distribution to the bone marrow and binding to TIM-3 was used to predict membrane-bound TIM-3 bone marrow occupancy. In addition, the total soluble TIM-3 (sTIM-3) kinetics and the pharmacokinetic (PK) exposure-response relationship in patients with hematologic malignancies were examined. At intravenous doses above 240 mg Q2w and 800 mg Q4w, we observed linear PK, a plateau in the accumulation of total sTIM-3, and a flat exposure-response relationship for both safety and efficacy. In addition, the model predicted membrane-bound TIM-3 occupancy in the bone marrow was above 95% in over 95% of patients. Therefore, these results support the selection of the 400 mg Q2w and 800 mg Q4w dosing regimens for the STIMULUS clinical trial program
Seizure reduction in TSC2-mutant mouse model by an mTOR catalytic inhibitor.
No full textTuberous sclerosis complex (TSC) is a neurodevelopmental disorder caused by autosomal-dominant pathogenic variants in either the TSC1 or TSC2 gene, and it is characterized by hamartomas in multiple organs, such as skin, kidney, lung, and brain. These changes can result in epilepsy, learning disabilities, and behavioral complications, among others. The mechanistic link between TSC and the mechanistic target of the rapamycin (mTOR) pathway is well established, thus mTOR inhibitors can potentially be used to treat the clinical manifestations of the disorder, including epilepsy.In this study, we tested the efficacy of a novel mTOR catalytic inhibitor (here named Tool Compound 1 or TC1) previously reported to be more brain-penetrant compared with other mTOR inhibitors. Using a well-characterized hypomorphic Tsc2 mouse model, which displays a translationally relevant seizure phenotype, we tested the efficacy of TC1.Our results show that chronic treatment with this novel mTOR catalytic inhibitor (TC1), which affects both the mTORC1 and mTORC2 signaling complexes, reduces seizure burden, and extends the survival of Tsc2 hypomorphic mice, restoring species typical weight gain over development.Novel mTOR catalytic inhibitor TC1 exhibits a promising therapeutic option in the treatment of TSC
Formulation intervention to overcome decreased kinetic solubility of a low Tg amorphous drug
No full textThis technical note investigated the loss of dissolution rate during accelerated stability studies with a dry blend capsule
formulation containing an amorphous salt of drug NVS-1 (Tg 76°C). After 6 m at 40°C/75%RH, dissolution of NVS-1 was
≤40% of initial value. Scanning electron microscope characterization of the undissolved capsule contents from samples stored
at 50°C/75%RH for 3 weeks showed agglomeration with a distinct “melt and fuse” morphology of particles. At elevated
temperature and humidity conditions, undesired sintering among the amorphous drug particles was observed. Humidity
plasticizes the drug as the stability temperature (T) gets closer to the glass transition temperature (Tg) of the amorphous salt
(i.e., smaller Tg-T); a decreased viscosity favors viscoplastic deformation and sintering of drug particles. When moisture is
adsorbed onto agglomerated drug particles, partial dissolution of the drug forms a viscous surface layer, further reducing
the rate of dissolution media penetration into the bulk solid, hence the slower dissolution rate. Formulation intervention
focused on the use of L-HPC and fumed silica as disintegrant and glidant and the removal of the hygroscopic crospovidone.
Reformulation improved dissolution performance at short-term accelerated stability conditions of 50°C (± 75%RH); how�ever, sintering to a lesser extent was still observed at high humidity, impacting the dissolution rate. We infer reducing the
impact of moisture at high humidity conditions in a formulation with a 34% drug load is challenging. Future formulation
eforts will focus on the addition of water scavengers, reducing drug load by ~50% to physically separate drug particles by
water-insoluble excipients, and optimizing disintegrant levels
Proteomic prediction of incident heart failure and its main subtypes.
No full textAim
To examine the ability of serum proteins in predicting future heart failure (HF) events, including HF with reduced or preserved ejection fraction (HFrEF or HFpEF), in relation to event time, and with or without considering established HF-associated clinical variables.
Methods and results
In the prospective population-based Age, Gene/Environment Susceptibility Reykjavik Study (AGES-RS), 440 individuals developed HF after their first visit with a median follow-up of 5.45 years. Among them, 167 were diagnosed with HFrEF and 188 with HFpEF. A least absolute shrinkage and selection operator regression model with non-parametric bootstrap were used to select predictors from an analysis of 4782 serum proteins, and several pre-established clinical parameters linked to HF. A subset of 8–10 distinct or overlapping serum proteins predicted different future HF outcomes, and C-statistics were used to assess discrimination, revealing proteins combined with a C-index of 0.80 for all incident HF, 0.78 and 0.80 for incident HFpEF or HFrEF, respectively. In the AGES-RS, protein panels alone encompassed the risk contained in the clinical information and improved the performance characteristics of prediction models based on N-terminal pro-B-type natriuretic peptide and clinical risk factors. Finally, the protein predictors performed particularly well close to the time of an HF event, an outcome that was replicated in the Cardiovascular Health Study.
Conclusion
A small number of circulating proteins accurately predicted future HF in the AGES-RS cohort of older adults, and they alone encompass the risk information found in a collection of clinical data. Incident HF events were predicted up to 8 years, with predictor performance significantly improving for events occurring less than 1 year ahead, a finding replicated in an external cohort study
Targeted Protein Degradation through E2 Recruitment.
No full textTargeted protein degradation (TPD) with proteolysis targeting chimeras (PROTACs), heterobifunctional compounds consisting of protein targeting ligands linked to recruiters of E3 ubiquitin ligases, has arisen as a powerful therapeutic modality to induce the proximity of target proteins with E3 ligases to ubiquitinate and degrade specific proteins in cells. Thus far, PROTACs have primarily exploited the recruitment of E3 ubiquitin ligases or their substrate adapter proteins but have not exploited the recruitment of more core components of the ubiquitin-proteasome system (UPS). In this study, we used covalent chemoproteomic approaches to discover a covalent recruiter against the E2 ubiquitin conjugating enzyme UBE2D─EN67─that targets an allosteric cysteine, C111, without affecting the enzymatic activity of the protein. We demonstrated that this UBE2D recruiter could be used in heterobifunctional degraders to degrade neo-substrate targets in a UBE2D-dependent manner, including BRD4 and the androgen receptor. Overall, our data highlight the potential for the recruitment of core components of the UPS machinery, such as E2 ubiquitin conjugating enzymes, for TPD, and underscore the utility of covalent chemoproteomic strategies for identifying novel recruiters for additional components of the UPS
GATA1 deletion in human pluripotent stem cells increases differentiation yield and maturity of neutrophils.
No full textHuman pluripotent stem cell (hPSC)-derived tissues can be used to model diseases in cell types that are challenging to harvest and study at-scale, such as neutrophils. Neutrophil dysregulation, specifically neutrophil extracellular trap (NET) formation, plays a critical role in the prognosis and progression of multiple diseases, including COVID-19. While hPSCs can generate limitless neutrophils (iNeutrophils) to study these processes, current differentiation protocols generate heterogeneous cultures of granulocytes and precursors. Here, we describe a method to improve iNeutrophil differentiations through the deletion of GATA1. GATA1 knockout (KO) iNeutrophils are nearly identical to primary neutrophils in form and function. Unlike wild-type iNeutrophils, GATA1 KO iNeutrophils generate NETs in response to the physiologic stimulant lipopolysaccharide, suggesting they are a more accurate model when performing NET inhibitor screens. Furthermore, through deletion of CYBB, we demonstrate that GATA1 KO iNeutrophils are a powerful tool in determining involvement of a given protein in NET formation
Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices.
No full textRecent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies. By drawing insights from workshops organized by the working group and attended by key stakeholders across the biopharma industry, academia, and regulatory agencies, this white paper provides a perspective from the IQ Consortium. The range of applications covered in this white paper encompass the following thematic topics: (i) AI/ ML-enabled Analytics for Pharmacometrics and Quantitative Systems Pharmacology (QSP) Workflows; (ii) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (iii) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; and (iv) AI/ML Utilization in Drug Discovery. Additionally, the paper offers a set of best practices to ensure an effective and responsible use of AI, including considering the context of use, explainability and generalizability of models, and having human-in-
the-loop. We believe that embracing the transformative power of AI in quantitative modeling while adopting a set of good practices can unlock new opportunities for innovation, increase efficiency, and ultimately bring benefits to patients