Abstract: Artificial intelligence (AI)-based method for cytotoxicity and repeated dose toxicity assessment is disclosed. A human MicroPhysiological System (hMPS) simulates microenvironment of the human system to evaluate immune responses to a test substance. Phenotype data are generated from a micrograph by treating hMPS with increasing the test substance concentrations. Micrographs are analyzed and IC50 of the specific test substance is calculated and determined using AI system. The IC50 value from the graphs is compared with that from LDH assay on hMPS treated with increasing the test substance concentrations. A 5% to 20% IC50 dose is administered for 2-4 hours, followed by a two-day recovery, with test substance administration on days 1, 4, and 7. Further, cells are collected on day 10 for downstream analysis. Cytotoxicity percentage is evaluated using AI system and validated by RNA transcriptomics, generating heat maps of toxicity-related genes to provide insights into toxicity pathways.

Abstract: The present invention discloses an artificial intelligence (AI)-based method for immunogenicity assessment. The method involves providing an in-vitro human hematopoietic microphysiology system (hMPS) to simulate a microenvironment that mimics the human hematopoietic system for evaluating immune responses to specific drug. The hMPS is analyzed using various laboratory instruments. The laboratory instruments provide an input data having phenotypic and biochemical responses. The inputs are evaluated by an AI system comprising AI powered in silico tools. The AI system is trained with a machine learning (ML) predictive model using a plurality of training datasets. Further, the platform enables the AI system to perform both qualitative and quantitative analyses of the drug's immune responses using benchmark patterns. The AI analyzes the qualitative and the quantitative data to produce a detailed report on immunogenicity.

Abstract: The present invention discloses a workstation solution for test predicting human safety concerns and efficacy measurements in a test agent. The workstation solution comprises, a real-time platform or human MicroPhysiological Systems (hMPS) unit, and a digital platform. The digital platform with embedded artificial intelligence (AI) is configured to predict safety (pharmacology) risks from phenotypes, genotypes and proteotype data sets acquired from test agents treated hMPS platform in a modular assay system. The digital platform is trained with phenotypes, genotypes, proteotypes, biochemical data sets as benchmark patterns and signals configured as positive or negative controls to provide a bandwidth to AI for detecting anomalies in a real-time assaying and for measuring the analyzed insights in real-time assaying. The workstation solution is a Digital Animal Free Testing (DAFT), which is a foundational scheme while the modular assay system is one of a derived application.

Abstract: A system and method for test predicting human neurovirulence and neurotoxicity risks is disclosed. The system comprises a real-time platform or TRANS-MSC (Configured Human induced Pluripotent Stem Cells) unit and a trained digital platform. The TRANS-MSC incubates the vaccine/biologic, drug/API, cosmetic/ingredient, anti-venom aliquots collected from the produced batches in the manufacturing system. The digital platform is embedded with artificial intelligence (AI) and machine learning (ML) modules, augmented with a robotic process automation framework. The AI modules predict human neurovirulence, human neurotoxicity patterns along with any adventitious microbial contaminants in the process. The AI and ML modules are trained with a plurality of TRANS-MSC acquired phenotype micrographs and a plurality of neurotoxic genes involved in viral, bacterial, fungal infections.

Abstract: A method of preparing an allogenic dendritic stem cell vaccine for treating ovarian cancers is disclosed. The method comprises, (a) collecting blood sample from an allogenic donor in a sterile collection bag, (b) separating viable mesenchymal stem cells inoculums by centrifuging the blood sample, suspending said mesenchymal stem cells inoculum in proliferation medium, plating the cells in a flask and incubated, and collecting cells adherent to the flask and further harvesting and expanding said adherent cells, (c) preparing myeloid dendritic cells by isolating, harvesting and culturing said blood sample source using myeloid dendritic isolation kit, (d) mixing the adherent cells obtained. Finally, (e) the composition of the adherent cells obtained in step (d) to the myeloid dendritic cells obtained in step (c) at a ratio of 90:10 and printing the cellular vectors with patient tumor derived heterogeneous progenitors.

Abstract: The present invention recommends a new approach of allogenic dendritic-stem cell vaccine platform to treat all kinds of ovarian cancers. It proposes the abundant allogenic donated cord blood source to harvest both dendritic special type and mesenchymal stem cells equilibrated package primed with patient's tumour antigens for a targeted treatment regime. The individual roles of dendritic cells and mesenchymal stem cells in immunomodulation is exploited in developing this new combinatorial platform of cellular vectors for targeted killing of tumour cells in the patient's body. The source proposed here in developing the technology is a biological discard and is available in profusion for proposed clinical application.