What is claimed is:
1. A method of treating or preventing an Helicobacter sp-associated disease in a patient in need of such treatment, comprising administering to the patient a therapeutically effective amount of a peptide comprising SEQ ID NO:1 in conjunction with a proton pump inhibitor (PPI), wherein the peptide is administered orally and remains active in the stomach for sufficient time to treat or prevent the H. pylori associated disease.
2. The method of claim 1, wherein the peptide is a fragment of gastrin or a synthetic analog of gastrin.
3. The method of claim 2, wherein the peptide is pentagastrin (PG), or an active fragment thereof, or a synthetic analog thereof.
4. The method of claim 3, wherein the peptide is the N-protected derivative of PG selected from methoxymethyl (MOM), -methoxyethoxymethyl (MEM), trialkylsilyl, triphenylmethyl (trityl), TIPSO, tert-butoxycarbonyl (t-BOC), ethoxyethyl (EE), F-MOC, and TROC.
5. The method of claim 1, wherein the Helicobacter sp-associated disease is an Helicobacter sp-associated gastrointestinal disease.
6. The method of claim 5, wherein the gastrointestinal disease is selected from gastric peptic ulcer, duodenal peptic ulcer, gastritis, duodenitis, non-ulcer dyspepsia and gastric carcinoma.
7. The method of claim 1, wherein the peptide is administered simultaneously, prior to or following the administration of the PPI.
8. The method of claim 1, wherein the PPI is selected from: rabeprazole, omeprazole, isomeprazole, lansoprazole, pantoprazole, leminoprazole, single enantiomers thereof, alkaline salts thereof and mixtures thereof.
9. The method of claim 1, wherein the Helicobacter sp is Helicobacter pylori.
10. An oral pharmaceutical composition comprising a pharmaceutically effective amount of pentagastrin (PG), an active fragment thereof, or a synthetic analog of PG, wherein the PG, active fragment thereof, or synthetic analog of PG comprises SEQ ID NO:1 and the composition is in a form suitable for oral administration.
11. The composition of claim 10, wherein the oral composition is in the form of a tablet, a capsule, solution, suspension, dispersion or emulsion.
12. The composition of claim 10, further comprising a PPI in a single dosage unit form.
13. The composition of claim 12, wherein the oral composition is in the form of a tablet or a capsule.
14. The composition of claim 13, wherein the tablet is a double-layered tablet, wherein the first layer comprises PG and the second layer comprises a PPI.
15. The composition of claim 12, wherein the PPI is selected from rabeprazole, omeprazole, lansoprazole and pantoprazole.
16. The composition of claim 13, wherein the tablet or capsule further comprising a controlled release agent, a bioadhesive polymer, an enteric-coated agent, a protease inhibitor or an antibiotic.
17. The composition of claim 15, wherein the PPI is granulated in enteric-coated form or non-enteric-coated form.
18. A method for treating a pathology in a mammal by inhibiting gastric acid secretion, the the method comprising administering to the mammal a peptide comprising SEQ ID NO:1 in conjunction with a proton pump inhibitor (PPI), wherein the peptide is administered orally and remains active in the stomach for sufficient time to inhibit gastric acid secretion, thereby treating the pathology.
19. The method of claim 18, wherein the peptide is a fragment of gastrin or a synthetic analog of gastrin.
20. The method of claim 19, wherein the peptide is pentagastrin (PG), an active fragment thereof, or a synthetic analog of PG.
21. The method of claim 20, wherein the peptide is the N-protected derivative of PG selected from methoxymethyl (MOM), -methoxyethoxymethyl (MEM), trialkylsilyl, triphenylmethyl (trityl), TIPSO, tert-butoxycarbonyl (t-BOC), ethoxyethyl (EE), F-MOC, and TROC.
22. The method of claim 18, wherein the pathology is selected from the group consisting of: reflux esophagitis, gastritis, duodenitis, gastric ulcer, duodenal ulcer, pathologies associated with nonsteroidal anti-inflammatory drugs (NSAID), non-ulcer Dyspepsia, gastro-esophageal reflux disease, acute upper gastrointestinal bleeding, stress ulceration, Helicobacter infections, Zollinger-Ellison syndrome (ZES), Werner’s syndrome, and systemic mastocytosis.
23. The method of claim 18, wherein the mammal is a human.
24. The method of claim 18, wherein the peptide is administered simultaneously, prior to or following the administration of the PPI.
25. The method of claim 18, wherein the PPI is selected from: rabeprazole, omeprazole, isomeprazole, lansoprazole, pantoprazole, leminoprazole, single enantiomers thereof, alkaline salts thereof and mixtures thereof.
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 computer processor implemented process for identifying a template associated with a queried customer from a group of customers, wherein a type and at least one subject of the template are selected based on (i) an influence score of the queried customer, (ii) an interest score of the queried customer, and (iii) a sensitivity profile of the queried customer, comprising:
identifying, in a processor of the computer, sensitivity variables, influence variables, and interest variables for the group of customers,
processing, in a processor of the computer, collected sensitivity data stored in memory for the queried customer, to position the queried customer on a first map,
identifying, in a processor of the computer, a sensitivity profile for the queried customer, based on the position of the queried customer on the first map,
computing, in a processor of the computer, an influence score of the queried customer based on the collected influence data for the queried customer,
computing, in a processor of the computer, an interest score of the queried customer based on the collected interest data for the queried customer,
processing, in a processor of the computer, the influence score and the interest score of the queried customer to position the queried customer on a second map,
identifying in a processor of the computer, an area of the second map in which the customer is located,
identifying a template type for the queried customer, based on the identified area of the second map in which the queried customer is located, and
identifying at least one template subject based on the identified sensitivity profile of the queried customer in the first map, and
outputting a template with the identified template type for the queried customer, and the identified at least one subject for the queried customer.
2: The computer processor implemented process of claim 1, further comprising:
collecting, in a memory of the computer, sensitivity data provided by each customer for the selected sensitivity variables,
collecting, in a memory of the computer, influence data for each customer for the selected influence variables,
collecting, in a memory of the computer, interest data for each customer for the selected interest variables.
3: The computer processor implemented process of claim 1, wherein the identifying, in a processor of the computer, of the sensitivity variables, influence variables, and interest variables includes:
identifying, in a processor of a computer, a set of variables;
validating the process of claim 1 with existing customers and templates; and
selecting a different set of variables until the process of claim 1 accurately matches existing customers and templates.
4: The computer processor implemented process of claim 1, wherein the second map is divided in four quadrants.
5: The computer processor implemented process of claim 4, wherein a first type of template corresponds to a first quadrant of the second map, a second type of template corresponds to a second quadrant of the second map, a third type of template corresponds to a third quadrant of the second map, and a fourth type of template corresponds to a fourth quadrant of the second map.
6: The computer processor implemented process of claim 1, wherein the selected sensitivity variables are at least one of: availability, lease cost, brand image, reliability, thrust, fuel burn, CO2 emission, NOx emission, engine price, noise level, time on wing, green technology, services contract, warranty and maintenance cost.
7: The computer processor implemented process of claim 1, wherein the selected interest variables are at least one of age of fleet, annual utilization, economic growth of a world region, and average age of aircraft operation.
8: The computer processor implemented process of claim 1, wherein the selected influence variables are at least one of owned aircraft count, operated aircraft count, annual flight count, and seat count.
9: The computer processor implemented process of claim 1, wherein the queried customer is queried by inputting an identification query, in a processor of the computer.
10: The computer processor implemented process of claim 1 wherein the selected template is selected from multiple templates stored in a database of the computer.
11: A system for identifying a template associated with a queried customer from a group of customers, wherein a type and at least one subject of the template are selected based on (i) an influence score of the queried customer, (ii) an interest score of the queried customer, and (iii) a sensitivity profile of the queried customer, comprising:
means for identifying sensitivity variables, influence variables, and interest variables for the group of customers,
means for processing collected sensitivity data stored in memory for the queried customer, to position the queried customer on a first map,
means for identifying a sensitivity profile for the queried customer, based on the position of the queried customer on the first map,
means for computing an influence score of the queried customer based on the collected influence data for the queried customer,
means for computing an interest score of the queried customer based on the collected interest data for the queried customer,
means for processing in a processor of the computer, the influence score and the interest score of the queried customer to position the queried customer on a second map,
means for identifying an area of the second map in which the customer is located,
means for identifying a template type for the queried customer, based on the identified area of the second map in which the queried customer is located, and
means for identifying at least one template subject based on the identified sensitivity profile of the queried customer in the first map, and
means for outputting a template with the identified template type for the queried customer, and the identified at least one subject for the queried customer.
12: A system for identifying a template associated with a queried customer from a group of customers, wherein a type and at least one subject of the template are selected based on (i) an influence score of the queried customer, (ii) an interest score of the queried customer, and (iii) a sensitivity profile of the queried customer, comprising:
means for identifying a sensitivity profile for the queried customer on a first map,
means for identifying an area of a second map of interest and influence in which the customer is located,
means for identifying a template type for the queried customer, based on the identified area of the second map in which the queried customer is located, and
means for identifying at least one template subject based on the identified sensitivity profile of the queried customer in the first map, and
means for outputting a template with the identified template type for the queried customer, and the identified at least one subject for the queried customer.