The current study addresses the growing demand for sustainable plant-based cheese alternatives by employing molecular docking and deep learning algorithms to optimize protein-ligand interactions. Focusing on key proteins (zein, soy, and almond protein) along with tocopherol and retinol, the goal was to improve texture, nutritional value, and flavor characteristics via dynamic simulations. The findings demonstrated that the docking analysis presented high accuracy in predicting conformational changes. Flexible docking algorithms provided insights into dynamic interactions, while analysis of energetics revealed variations in binding strengths. Tocopherol exhibited stronger affinity (-5.8Kcal/mol) to zein compared to retinol (-4.1Kcal/mol). Molecular dynamics simulations offered comprehensive insights into stability and behavior over time. The integration of machine learning algorithms improved the classification and the prediction accuracy, achieving a rate of 71.59%. This study underscores the significance of molecular understanding in driving innovation in the plant-based cheese industry, facilitating the development of sustainable alternatives to traditional dairy products. 

Enhancing plant-based cheese formulation through molecular docking and dynamic simulation of tocopherol and retinol complexes with zein, soy and almond proteins via SVM-machine learning integration.

ABSTRACT Introduction Diffuse large B-cell lymphoma (DLBCL) is an aggressive and clinically heterogeneous malignancy originating from B-cells with up to 40% of patients experiencing primary refractory disease or relapse after first-line treatment. However, the past 5 years have seen a flurry of new drug approvals for DLBCL anchored upon new immune therapies, including chimeric antigen receptor (CAR) T-cells and antibody-based therapies. Areas covered This article summarizes recent advances in the treatment of DLBCL, including in the first line and relapsed and refractory setting (second-line and beyond). A literature search was conducted for publications relevant to the immunotherapeutic approach to DLBCL from 2000 through March 2023 within PubMed and articles were reviewed. The search terms were immunotherapy, monoclonal antibodies, chimeric antigen receptor modified T-cell (CAR-T), and classification of DLBCL. Relevant clinical trials and pre-clinical studies exploring the strengths and weaknesses of current immune therapies against DLBCL were chosen. We additionally explored how intrinsic differences amongst DLBCL subtype biology and endogenous host immune recruitment contribute to variable therapeutic efficacy. Expert Opinion Future treatments will minimize chemotherapy exposure and be chosen by underlying tumor biology, paving the way for the promise of chemotherapeutic free regimens and improved outcomes for poor-risk subgroups.

Immune-based therapies in diffuse large B-cell lymphoma

Currently, a significant percentage of patients with DLBCL are refractory or relapse after a first line of immunochemotherapy. Second relapses after autologous stem cell transplantation or chimeric antigen receptor T-cell therapies present few treatment options and do not yield good results. New molecules have entered the immunotherapy arsenal. Loncastuximab tesirine comprises a humanized anti-CD19 monoclonal conjugated antibody, which consists of an anti-CD19 antibody and cytotoxic alkylating agent, SG3199. Several studies have proven its efficacy in the treatment of refractory cases of DLBCL with a good safety profile, with the main adverse effects being neutropenia, thrombopenia, and liver enzyme involvement. In this review, we explain the mechanism of action of this molecule, the clinical data that have led to its acceptance by the FDA, and the new therapeutic options that are proposed in association with this drug. 

Loncastuximab Tesirine in the Treatment of Relapsed or Refractory Diffuse Large B-Cell Lymphoma

Cancer is a global health threat, with its treatment modalities transitioning from single therapies to integrated treatments. This paper systematically explores the key technological systems in modern cancer treatment and their application value. Modern cancer treatment relies on four core technological pillars: omics, bioinformatics, network pharmacology (NP), and molecular dynamics (MD) simulation. Omics technologies integrate various biological molecular information, such as genomics, proteomics and metabolomics, providing foundational data support for drug research. But the differences in data and the challenges of integrating it often lead to biased predictions, and that’s a big limitation for this technology. Bioinformatics utilizes computer science and statistical methods to process and analyze biological data, aiding in the identification of drug targets and the elucidation of mechanisms of action. It is important to note that the prediction accuracy largely depends on the algorithm chosen. Consequently, this dependence may affect the reliability of the research results. NP, based on systems biology, studies drug-target-disease networks, revealing the potential for multitargeted therapies. That said, this method may overlook important aspects of biological complexity, such as variations in protein expression. This oversight can lead to overestimating the effectiveness of multi-targeted therapies, resulting in false positives in efficacy assessments, which somewhat limits its practical usefulness. MD simulation examines how drugs interact with target proteins by tracking atomic movements, thus enhancing the precision of drug design and optimization. Nevertheless, this technology faces practical challenges, such as high computational costs and sensitivity of model accuracy to the parameters of the force field. The synergistic application of these technologies significantly shortens the drug development cycle and promotes precision and personalization in cancer therapy, bringing new hope to patients for successful treatment. However, researchers still face challenges like the variability of data. Future efforts need to use Artificial Intelligence (AI) to establish standardized data integration platforms, develop multimodal analysis algorithms, and strengthen preclinical-clinical translational research to drive breakthrough advancements in cancer treatment. With the ongoing technological improvements, the vision of personalized medicine—tailored treatments based on individual patient characteristics—will gradually be realized, significantly enhancing treatment efficacy and improving patients’ quality of life. 

pdf/strategies-for-the-drug-development-of-cancer-therapeutics-kukp6pb1udzb.pdf

Strategies for the drug development of cancer therapeutics

Abstract Drug development is a long and costly process, with a high degree of uncertainty from the identification of a drug target to its market launch. Targeted drugs supported by human genetic evidence are expected to enter phase II/III clinical trials or be approved for marketing more quickly, speeding up the drug development process. Currently, genetic data and technologies such as genome‐wide association studies (GWAS), whole‐exome sequencing (WES), and whole‐genome sequencing (WGS) have identified and validated many potential molecular targets associated with diseases. This review describes the structure, molecular biology, and drug development of human genetics‐based validated beneficial loss‐of‐function (LOF) mutation targets (target mutations that reduce disease incidence) over the past decade. The feasibility of eight beneficial LOF mutation targets (PCSK9, ANGPTL3, ASGR1, HSD17B13, KHK, CIDEB, GPR75, and INHBE) as targets for drug discovery is mainly emphasized, and their research prospects and challenges are discussed. In conclusion, we expect that this review will inspire more researchers to use human genetics and genomics to support the discovery of novel therapeutic drugs and the direction of clinical development, which will contribute to the development of new drug discovery and drug repurposing.

Drug development advances in human genetics‐based targets

Hypercholesterolemia is a significant risk factor for atherosclerotic cardiovascular disease (ASCVD). Successful management of cholesterol metabolism disorders can prevent these ASCVD effectively. Asialoglycoprotein receptor 1 (ASGR1) is the main subtype of sialoglycoprotein receptor, which is specifically expressed in the liver and mediates the endocytosis of blood asialoglycoprotein to lysosome degradation. Recently, ASGR1 has been reported as a new therapeutic target for the treatment of hypercholesterolemia. In this study, the main aim was to identify natural ASGR1 inhibitors from plant food chemicals library through pharmacophore and docking based virtual screening. Total 14 phytochemicals of potential ASGR1 inhibitors were identified, which presented docking affinity higher than control compound through docking based virtual screening. The docking pose showed the top three hits interacted residues were located at active pocket of ASGR1 with hydrogen bonds, hydrophobic interactions and electrostatic interactions. The top three hits (ZINC85664954, ZINC169372863, and ZINC195764535) were then subjected to 200 ns molecular dynamics simulation to evaluate the stability of docked complexes. These results showed that selected phytochemicals bound to ASGR1 with higher stability than control compound. Binding free energy of each docked complex was calculated by the Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) method. The binding free energy of ZINC85664954, ZINC169372863, ZINC195764535, and control-ASGR1 docked complexes were -18.359, -13.303, -14.389, and -6.229 kcal/mol, respectively. This indicated that selected hits bound to ASGR1 with higher affinity than control compound. Network pharmacology analysis shows that these phytochemicals have obvious multiple-effects and can regulate various biochemical pathways related to hypercholesterolemia. Besides, selected phytochemicals have suitable pharmacokinetics properties, suggesting that these compounds may be potential drug candidates. This study may be contributed to rational design of novel ASGR1 inhibitors for treatment of hypercholesterolemia. 

In silico discovery of food-derived phytochemicals against asialoglycoprotein receptor 1 for treatment of hypercholesterolemia: Pharmacophore modeling, molecular docking and molecular dynamics simulation approach.

Introduction: Patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) typically have poor outcomes following standard treatment. Loncastuximab tesirine (loncastuximab tesirine-lpyl; Lonca), an antibody-drug conjugate (ADC) comprising a humanized anti-CD19 monoclonal antibody conjugated to a pyrrolobenzodiazepine (PBD) dimer toxin, received accelerated (US) and conditional (EU) approval in R/R DLBCL after ≥2 lines of systemic therapy, based on data from the phase 2 LOTIS-2 trial (Caimi PF et al. Lancet Oncol. 2021;22[6]:790). Rituximab (R), an anti-CD20 monoclonal antibody, is part of standard immunotherapy for DLBCL, both as frontline therapy and in subsequent treatments. Preclinical evidence suggests the addition of R to anti-CD19 ADC therapy may result in prolonged tumor control (Ryan MC et al. Blood. 2017;130[18]:2018). LOTIS-5 aims to evaluate Lonca-R vs standard immunotherapy of R + gemcitabine + oxaliplatin (R-GemOx) in R/R DLBCL. Methods: This is a phase 3, randomized, open-label, 2-part, 2-arm, multicenter study of Lonca-R in patients with R/R DLBCL (NCT04384484). The study consists of a safety run-in phase with Lonca-R (part 1) and a randomized phase evaluating efficacy and safety of Lonca-R vs R-GemOx (part 2). Approximately 350 patients will be enrolled across both parts: part 1 has completed; part 2 will enroll approximately 330 patients (randomized 1:1) to achieve 262 events for the primary end point analysis of progression-free survival by independent central review. Secondary end points include overall survival, overall response rate (2014 Lugano classification), complete response rate, duration of response, frequency and severity of adverse events, change from baseline in laboratory values, concentration and pharmacokinetic parameters of Lonca (conjugated and total antibody and unconjugated PBD), and changes in patient-reported outcomes from baseline. The dosing regimen for Lonca-R is Lonca 150 μg/kg + rituximab 375 mg/m2 every 3 weeks (Q3W) for 2 cycles, then Lonca 75 μg/kg + rituximab 375 mg/m2 Q3W for up to 6 cycles. The dose regimen of R-GemOx is rituximab 375 mg/m2, gemcitabine 1000 mg/m2, and oxaliplatin 100 mg/m2 every 2 weeks for up to 8 cycles. Key eligibility criteria include age ≥18 years, pathologic diagnosis of DLBCL (including patients with DLBCL transformed from indolent lymphoma) or high-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements, ≥1 line of prior systemic therapy, previous stem cell transplant >30 days (autologous) or >60 days (allogenic) prior to start of study drug or stem cell transplant ineligibility, and measurable disease. The randomized part of LOTIS-5 began in January 2022 and has an estimated primary completion date of June 2025; enrollment continues, with a total of 45 active sites across the US, Canada, Spain, France, Belgium, Italy, Switzerland, Czech Republic, Poland, and China. The research was funded by: ADC Therapeutics SA; medical writing: CiTRUS Health Group Keywords: molecular targeted therapies, combination therapies, ongoing trials Conflicts of interests pertinent to the abstract C. Carlo-Stella Consultant or advisory role ADC Therapeutics, Celgene/Bristol Myers Squibb, Karyopharm, Novartis, Sanofi, Roche, Merck Sharp & Dohme, Scenic Biotech Honoraria: Merck Sharp & Dohme, Janssen Oncology, AstraZeneca, Celgene, Incyte, Gilead Sciences, Roche M. Chung Stock ownership: The Oncology Institute A. Stathis Honoraria: Eli Lilly, Bayer, Roche, Novartis, Janssen Oncology, AstraZeneca Research funding: ADC Therapeutics , Pfizer, Roche, Novartis, Bayer, Eli Lilly, MEI Pharma, Cellestia, Debiopharm Group, Merck/MSD, Abbvie, Amgen, AstraZeneca, Incyte, Loxo, Philogen Y. Wang Employment or leadership position: ADC Therapeutics Stock ownership: Johnson and Johnson (Family Member) L. Wang Employment or leadership position: ADC Therapeutics M. Hamadani Consultant or advisory role AbGenomics, ADC Therapeutics, Celgene, Incyte, Janssen R&D, Omeros, Pharmacyclics, TeneoBio, Verastem Research funding: Astellas Pharma, Spectrum Pharmaceuticals, Takeda Other remuneration: Speakers’ bureau for AstraZeneca, BeiGene, Sanofi Genzyme 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hon.3166_OT29

Lotis‐5, an ongoing phase 3 randomized study of loncastuximab tesirine with rituximab (lonca‐r) versus immunochemotherapy in patients with r/r dlbcl

Background: Background: Antithymocyte globulin (ATG) can improve the outcome of HLA mismatched unrelated transplantation, but there is limited data to compare HLA mismatched unrelated transplantation with matched unrelated transplantation both under condition of ATG-containing regimen. Aims: Aims: To compare HLA mismatched unrelated transplantation with matched unrelated transplantation both under condition of ATG-containing regimen. Methods: Methods: A total of 186 patients with hematological malignancies who underwent a first allo-SCT from an HLA-matched (10/10) or mismatched (9/10 or 8/10) unrelated donor from Chinese bone marrow bank and Taiwan Tzu Chi bone marrow bank following ATG-containing condition was retrospectively analyzed. A total dose of 10mg/kg ATG-Genzyme or 20mg/kg ATG- Fresenius was given from days -5 to days -2. 114 patients received HLA 10/10 fully matched, while 68 received a 9/10 and 4 received an 8/10 mismatched unrelated donor allo-SCT. OS, RFS and GRFS was calculated by the Kaplan-Meier method and the differences between groups were assessed by the log-rank test, and multivariate analysis was carried out by Cox regression model. The cumulative incidence of relapse, NRM, grade II-IV aGVHD, cGVHD and extensive cGVHD was estimated by competing risk model and compared by Gray test. Multivariate analysis was conducted by Fine and Gray regression model. Results: Results: the median follow-up time of HLA matched transplantation and mismatched transplantation were 24 months and 21 months, respectively (P = 0.333). Univariate analysis showed that there was no significant difference in OS (78.7% vs 73.9%, P = 0.766), RFS (61.9% vs 54.8%, P = 0.464) and GRFS (46.6% vs 30.7%, P = 0.105) between HLA matched and mismatched transplantation. Multivariate analysis showed that HLA mismatch did not affect OS, RFS or GRFS (P = 0.654, P = 0.811, and P = 0.114, respectively). Univariate analysis showed no significant difference in CIR (26.1% vs 32.6%, P = 0.531), NRM (12.0% vs 12.6%, P = 0.800), cumulative incidence of cGVHD (45.3% vs 27.7%, P = 0.293) and cumulative incidence of extensive cGVHD (26.7% vs 23.4%, P = 0.635) between matched and mismatched transplantation. The cumulative incidence of grade II-IV aGVHD was higher in HLA mismatched transplantation (4.4% vs 12.3%, P = 0.046). Multivariate analysis showed that HLA mismatch was not a risk factor for CIR, NRM, cGVHD and extensive cGVHD (P = 0.44, P = 0.88, P = 0.32, P = 0.9, respectively). HLA mismatched transplantation had higher risk of grade II-IV aGVHD (HR = 2.98, P = 0.047). stem

https://doi.org/10.1097/01.hs9.0000848316.14875.0f

P1364: hla mismatched unrelated stem cell transplantation using antithymocyte globulin is a suitable alternative for patients lacking hla-matched unrelated donors: single center long-term follow-up data anal

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In silico discovery of food-derived phytochemicals against asialoglycoprotein receptor 1 for treatment of hypercholesterolemia: Pharmacophore modeling, molecular docking and molecular dynamics simulation approach.

Lotis‐5, an ongoing phase 3 randomized study of loncastuximab tesirine with rituximab (lonca‐r) versus immunochemotherapy in patients with r/r dlbcl

P1364: hla mismatched unrelated stem cell transplantation using antithymocyte globulin is a suitable alternative for patients lacking hla-matched unrelated donors: single center long-term follow-up data anal