1. A lyophilized pharmaceutical composition comprising:
(a) a unit dose of 0.5 mg or 1.5 mg of epoprostenol or a salt thereof;
(b) arginine; and
(c) sodium hydroxide,
wherein said lyophilized pharmaceutical composition is (i) formed from a bulk solution having a pH of 13 or higher and (ii) capable of being reconstituted for intravenous administration with an intravenous fluid.
2. The lyophilized pharmaceutical composition of claim 1, wherein the ratio of the epoprostenol to arginine is about 1:25 to about 1:200 by weight.
3. The lyophilized pharmaceutical composition of claim 1, further comprising a bulking agent.
4. The lyophilized pharmaceutical composition of claim 3, wherein the bulking agent is selected from the group consisting of hydroxyl ethyl starch (HES), sorbitol, lactose, dextran, maltose, mannose, ribose, sucrose, mannitol, trehalose, lactose, dextran, cyclodextrin, glycine, and polyvinylpyrrolidine (PVP).
5. The lyophilized pharmaceutical composition of claim 3, wherein the bulking agent is present at about 1-10%.
6. The lyophilized pharmaceutical composition of claim 1, wherein the epoprostenol salt is epoprostenol sodium.
7. The lyophilized pharmaceutical composition of claim 3, wherein the bulking agent is selected from the group consisting of dextran, sucrose and mannitol.
8. The lyophilized pharmaceutical composition of claim 3, wherein the bulking agent is sucrose.
9. The lyophilized pharmaceutical composition of claim 3, wherein the bulking agent is mannitol.
10. The lyophilized pharmaceutical composition of claim 1, further comprising a sealed sterile vial containing the composition.
11. A lyophilisate formed from a bulk solution comprising:
(a) epoprostenol or a salt thereof;
(b) arginine;
(c) sodium hydroxide; and
(d) water,
wherein the bulk solution has a pH of 13 or higher, and wherein said lyophilisate is capable of being reconstituted for intravenous administration with an intravenous fluid.
12. The lyophilisate of claim 11, wherein the ratio of epoprostenol sodium to arginine is about 1:25 to about 1:200.
13. The lyophilisate of claim 11, wherein said solution further comprising a bulking agent.
14. The lyophilisate of claim 13, wherein the bulking agent is selected from the group consisting of hydroxyl ethyl starch (HES), sorbitol, lactose, dextran, maltose, mannose, ribose, sucrose, mannitol, trehalose, lactose, dextran, cyclodextrin, glycine, and polyvinylpyrrolidine (PVP).
15. The lyophilisate of claim 13, wherein the bulking agent is present at about 1-10%.
16. The lyophilisate of claim 11, wherein the epoprostenol salt is epoprostenol sodium.
17. The lyophilisate of claim 13, wherein the bulking agent is selected from the group consisting of dextran, sucrose and mannitol.
18. The lyophilisate of claim 13, wherein the bulking agent is sucrose.
19. The lyophilisate of claim 13, wherein the bulking agent is mannitol.
20. The lyophilized pharmaceutical composition of claim 1, wherein the arginine is present in the amount of 50 mg.
21. The lyophilized pharmaceutical composition according to claim 1, comprising:
(a) a unit dose of 0.5 mg or 1.5 mg of epoprostenol or a salt thereof;
(b) 50 mg of arginine;
(c) mannitol; and
(d) sodium hydroxide.
22. The lyophilized pharmaceutical composition according to claim 1, comprising:
(a) a unit dose of 0.5 mg or 1.5 mg of epoprostenol or a salt thereof;
(b) 50 mg of arginine;
(c) sucrose; and
(d) sodium hydroxide.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A computerized method operable in a computer system to enable an automated updating of computer applications based on artificial intelligence methods comprises the steps of: (a) collecting automation information related to a current application; (b) locating application controls; (c) scanning the computer application to extract data on processing power, load, footprint, and performance attributes; (d) using the data in an automation profile generator for determining a system automation profile; (e) using an artificial intelligence based modeler for analyzing input and output data; (f) and applying the data to an artificial intelligence model for training and predicting performance, and wherein if the training and predicting function is not satisfied during test, the artificial intelligence model is adjusted and the process of creating a satisfactory performance model is repeated, until the training and predicting function is satisfied; (g) then merging the artificial intelligence model within a cloud based database; (h) adjusting the artificial intelligence model to achieve an updated automation criteria with optimal values, wherein the optimal values provide input to an automation criteria library for storing and updating a prior automation criteria; and (i) exporting the upgraded automation criteria values for incorporation in a computer-to-be-updated, to achieve a reliable automatic update.
2. The method of claim 1, wherein the artificial intelligence model utilizes one of supervised and unsupervised learning methods, regression analysis, Bayesian statistical models, and clustering algorithms.
3. The method of claim 1, further including comparing system inputs and automation outputs from a local system to stored results to obtain the optimal automation fit for the computer-to-be-updated under examination, as a candidate for updating.
4. The method of claim 1, further including determining system automation profiles via one or more of a computer operating system, processing power, processing load, speed, processor type, manufacture application footprint, characteristics and specific application.
5. The method of claim 1, further including constructing artificial intelligence based learning mechanisms to derive key automation criteria.
6. The method of claim 1, further including combining artificial intelligence based learning on a local system, with broader learning from multiple similar systems, to provide recommendations on automation criteria such as optimal automation speed, best automation technique, and recommendations for optimal delays.
7. The method of claim 1, further including creating portable automation criteria libraries, correlated to a system with a specific automation profiles.
8. A non-transitory computer-readable medium having stored thereon computer-readable instructions for creating adapters that enable new and updated applications including steps of: (a) collecting automation information related to a current application; (b) locating application controls; (c) scanning the computer application to extract data on processing power, load, footprint, and performance attributes; (d) using the data in an automation profile generator for determining a system automation profile; (e) using an artificial intelligence based modeler for analyzing input and output data; (f) and applying the data to an artificial intelligence model for training and predicting performance, and wherein if the training and predicting function is not satisfied during test, the artificial intelligence model is adjusted and the process of creating a satisfactory performance model is repeated, until the training and predicting function is satisfied; (g) adjusting the artificial intelligence model to achieve an updated automation criteria with optimal values, wherein the optimal values provide input to an automation criteria library for storing and updating a prior automation criteria; and (h) exporting the upgraded automation criteria values for incorporation in a computer-to-be-updated, to achieve a reliable automatic update.
9. A computer system for creating adapters that enable new and updated applications comprising: (a) an automation profile generator for determining a computer to be updated computer automation profile as an aggregate function of the data provided by the system’s application inspector; (b) an artificial intelligence based modeler for applying artificial intelligence mechanisms and techniques to determine optimal automation criteria based on the system automation profile; (c) a recommendation engine for merging one or more artificial intelligence based modeler results, for performing an aggregate analysis, for adjusting an automation criteria to optimize values; (d) an automation criteria library for storage, update and retrieval; (e) a learning and update mechanism for updating the automation criteria over the entire period of time during which automation is performed on the computer to be updated; (f) a library porting mechanism for exporting the automation criteria library to a computer-to-be-updated, to achieve a reliable automatic update.