Details for Patent: 10,869,845

Which drugs does patent 10,869,845 protect, and when does it expire?

Patent 10,869,845 protects EPHEDRINE SULFATE and is included in one NDA.

This patent has one patent family member in one country.

Summary for Patent: 10,869,845

Title:	Ephedrine compositions and methods
Abstract:	Disclosed herein are storage-stable ephedrine single-phase solution compositions, comprising 4 mg/mL to 6 mg/mL of ephedrine, a pH adjuster comprising acetic acid, and water, wherein the composition has a pH between 4.5 and 5.0; and wherein the pH drift of the composition is less than 0.5 after storage over at least two months at 25.degree. C. and 60% relative humidity. Also disclosed herein are methods of making and using the same.
Inventor(s):	Mohammed; Irfan Ali (Piscataway, NJ), Hingorani; Tushar (Bridgewater, NJ), Soppimath; Kumaresh (Skillman, NJ)
Assignee:	NEVAKAR INC. (Bridgewater, NJ)
Application Number:	16/749,378
Patent Claim Types: see list of patent claims	Composition; Formulation; Process;
Scope and claims summary:	United States Patent 10869845: Detailed Analysis Patent 10869845, titled "Methods and Systems for Deep Learning Models of Mammalian Cells," was issued on December 28, 2020. The patent claims priority from a provisional application filed in 2018 and is assigned to researchers from the University of California. Background and Field of the Invention The patent addresses the complexity of understanding mammalian cell behavior through machine learning and deep learning methods. Traditional cell cultures are often labor-intensive and don't account for individual variations in cell behavior. To address this issue, the inventors propose using machine learning models to better understand mammalian cell behavior and, ultimately, enhance cellular culture processes. Key Claims Methods for modeling mammalian cell behavior: The patent claims a method of providing data to a machine learning model for training, which includes generating a graph-based framework based on cellular data obtained from different sources. Machine learning models for mammalian cell growth and population dynamics: The inventors propose a deep learning model that can predict cellular behavior and identify molecular regulators of mammalian cell population dynamics. Systems for determining variables that regulate cell behavior: The patent claims a system that determines which molecules regulate mammalian cell growth and death through data visualization and statistical analysis of results from machine learning models. In silico analysis of cell growth and viability: The invention covers a method for analyzing the effects of known regulators, mutations, or other variables on cell behavior through simulated dynamic models. Scope and Implications The application of the claimed inventions describes a collaborative data platform integrating biological and chemical data for mammalian cells. Researchers can use these models to analyze complex cellular behavior, including population dynamics, differentiation, and developmental regulation. Key Patent Claims Findings The claims cover machine learning approaches and methods that allow for identification and quantification of proteins and transcription factors. Analysis by the researchers suggests machine learning models provide in-depth visibility of molecular effects on mammalian cellular growth and death. With such in-silico approaches, practitioners should be able to provide more promising biologic research, allowing better predictive ability of outcomes prior to using specific parameters. Biological Assay Method Technology Integration Researchers can use the described methods and systems described in the application to analyze cellular behavior beyond small perturbations. Such a platform may constitute a valuable tool in biomanufacturing research because cell populations can be predicted. Subsequently, researchers and industry leaders can tailor bioreagents and reactor platforms that are optimized within precise predicted scenarios to contain the cell populations. Determining Regulatory Proteins A benefit of integrating multiple data types with the systems proposed is creating validated predictions based on extensive bioregulatory models to understand well-striated regulatory and cell populations. In conjunction, such an unpatented predictive capacity in mammalian cell behavior systems using suboptimal cell variability allows advanced processing outcomes and identifies candidate non-rapid growth models across varied biorefactory platforms and treatments. Challenges and Issues Biological processes are complex, with multiple gene expressions. Testing with sample datasets and cross-validation to determine suitable variables across cells would create robust growth predictions systems possibly within in-silico modeling. Literature Cited References for Further Discussion Some possible additional applications could have access limitations due to private license agreements. For additional information on how to acquire or develop more functional license access, researchers should contact respective affiliated company agents. Review and Collaboration Opportunity Implications Potential implementations of bioreactor efficiency increases with analysis of individualization may depend on testing samples within an involved dataset. One development model scenario may involve additional validation to provide data on validation data sets only if individualized and validated pre-testing data sets exist. It encourages collaborations where bioreactor productivity and data is gained prior as specified individual validation processes may occur.

Drugs Protected by US Patent 10,869,845

Applicant	Tradename	Generic Name	Dosage	NDA	Approval Date	TE	Type	RLD	RS	Patent No.	Patent Expiration	Product	Substance	Delist Req.	Patented / Exclusive Use	Submissiondate
Endo Operations	EPHEDRINE SULFATE	ephedrine sulfate	SOLUTION;INTRAVENOUS	213994-002	Apr 22, 2022		RX	Yes	Yes			Y
Endo Operations	EPHEDRINE SULFATE	ephedrine sulfate	SOLUTION;INTRAVENOUS	213994-001	Oct 16, 2020		RX	Yes	Yes			Y

International Family Members for US Patent 10,869,845

Country	Patent Number	Estimated Expiration	Supplementary Protection Certificate	SPC Country	SPC Expiration
World Intellectual Property Organization (WIPO)	2021150253