A method is provided for lowering blood pressure in a subject in need thereof by administering a dihydropyridine-type calcium channel blocker pharmaceutical composition to a subject qualified for over-the-counter access to the dihydropyridine-type calcium channel blocker pharmaceutical composition. In some embodiments, the dihydropyridine-type calcium channel blocker pharmaceutical composition includes isradipine, nifedipine, or nisoldipine. In some embodiments, the dihydropyridine-type calcium channel blocker pharmaceutical composition includes 3-O-ethyl 5-O-methyl 2-(2-aminoethoxymethyl)-4-(2-chlorophenyl)-6-methyl-1,4-dihydropyridine-3,5-dicarboxylate or a pharmaceutically acceptable salt thereof.

A method is provided for lowering blood pressure in a subject in need thereof by administering a dihydropyridine-type calcium channel blocker pharmaceutical composition to a subject qualified for over-the-counter access to the dihydropyridine-type calcium channel blocker pharmaceutical composition. In some embodiments, the dihydropyridine-type calcium channel blocker pharmaceutical composition includes isradipine, nifedipine, or nisoldipine. In some embodiments, the dihydropyridine-type calcium channel blocker pharmaceutical composition includes 3-O-ethyl 5-O-methyl 2-(2-aminoethoxymethyl)-4-(2-chlorophenyl)-6-methyl-1,4-dihydropyridine-3,5-dicarboxylate or a pharmaceutically acceptable salt thereof.

A manufacturing system for a biopharmaceutical product includes first and second sets of biopharmaceutical manufacturing equipment located at a first and second enterprise sites, and memory configured to store an enterprise configuration and a process specification. The enterprise configuration includes records of one or more equipment parameters of the multiple pieces of equipment of the first and second sets of biopharmaceutical manufacturing equipment. At least one processor is configured to execute instructions determine whether at least one of the first enterprise site and the second enterprise site is capable of manufacturing the biopharmaceutical product, transmit a generated set of instructions to the determined at least one of the first enterprise site and the second enterprise site, and operate the multiple pieces of equipment at the determined at least one of the first enterprise site and the second enterprise site to manufacture the biopharmaceutical product.

A manufacturing system for a biopharmaceutical product includes first and second sets of biopharmaceutical manufacturing equipment located at a first and second enterprise sites, and memory configured to store an enterprise configuration and a process specification. The enterprise configuration includes records of one or more equipment parameters of the multiple pieces of equipment of the first and second sets of biopharmaceutical manufacturing equipment. At least one processor is configured to execute instructions determine whether at least one of the first enterprise site and the second enterprise site is capable of manufacturing the biopharmaceutical product, transmit a generated set of instructions to the determined at least one of the first enterprise site and the second enterprise site, and operate the multiple pieces of equipment at the determined at least one of the first enterprise site and the second enterprise site to manufacture the biopharmaceutical product.

Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for generating an output indicating differences in the data stored in disparate data storage devices and/or for reconciling data stored in disparate data storage devices. In an embodiment, a server loads a first subset of a first set of data corresponding to one or more first columns and a second subset of a second set of data corresponding to one or more second columns into a data repository. The server identifies one or more differences between the first subset of data and the second subset of data in the data repository, and causes display of the one or more differences. The server may generate an output including the first and second sets of data, and a visual indicator indicating each of the one or more differences and causes display of the output.

A computerized method for transforming an interactive graphical user interface according to machine learning includes selecting a persona, loading a data structure associated with the selected persona, and generating the interactive graphical user interface. The method includes, in response to a user selecting a first selectable element, inputting a first set of explanatory variables to a first trained machine learning model to generate a first metric, and transforming the user interface according to the selected persona and the first metric. The method includes, in response to the user selecting a second selectable element, inputting a second set of explanatory variables to a second trained machine learning model to generate a second metric, and transforming the user interface according to the selected persona and the second metric. In various implementations, first metric is a first probability of the persona being approved for a first prior authorization prescription.

A computerized method for transforming a user interface according to machine learning includes selecting a persona and determining whether a first condition is true for an associated data structure. In response to determining the first condition is true, the method includes determining whether a second condition is true. In response to determining the second condition is not true, the method includes loading a first trained machine learning model, inputting a first set of explanatory variables to generate a first metric, and transforming the user interface according to the first metric. In response to determining the second condition is true, the method includes determining whether a third condition is true. In response to determining the third condition is true, loading a second trained machine learning model, inputting a second set of explanatory variables to generate a second metric, and transforming the user interface according to the second metric.

A computerized method for transforming a user interface according to machine learning includes selecting a persona and determining whether a first condition is true for an associated data structure. In response to determining the first condition is true, the method includes determining whether a second condition is true. In response to determining the second condition is not true, the method includes loading a first trained machine learning model, inputting a first set of explanatory variables to generate a first metric, and transforming the user interface according to the first metric. In response to determining the second condition is true, the method includes determining whether a third condition is true. In response to determining the third condition is true, loading a second trained machine learning model, inputting a second set of explanatory variables to generate a second metric, and transforming the user interface according to the second metric.

Methods and systems for presenting a proactive audiovisual query using a virtual assistant software application. The methods correspond to retrieving user experience data associated with a user profile for the virtual assistant software application, wherein the user experience data define one or more user experience events for the user profile. The methods further include retrieving subject matter domain data associated with the one or more user experience events, wherein the subject matter domain data define one or more probabilistic event effects for the one or more user experience events and determining, based on the user experience data and the subject matter domain data, one or more optimal query items, wherein the one or more optimal query items are associated with at least one of the one or more probabilistic event effects. The methods further include generating the proactive audiovisual query in accordance with the one or more optimal query items.

Methods and systems for presenting a proactive audiovisual query using a virtual assistant software application. The methods correspond to retrieving user experience data associated with a user profile for the virtual assistant software application, wherein the user experience data define one or more user experience events for the user profile. The methods further include retrieving subject matter domain data associated with the one or more user experience events, wherein the subject matter domain data define one or more probabilistic event effects for the one or more user experience events and determining, based on the user experience data and the subject matter domain data, one or more optimal query items, wherein the one or more optimal query items are associated with at least one of the one or more probabilistic event effects. The methods further include generating the proactive audiovisual query in accordance with the one or more optimal query items.