000			03226nam a22004935i 4500
001			978-3-642-39765-3
003			DE-He213
005			20140220082519.0
007			cr nn 008mamaa
008			131127s2014 gw | s |||| 0|eng d
020			_a9783642397653 _9978-3-642-39765-3
024	7		_a10.1007/978-3-642-39765-3 _2doi
050		4	_aRM1-950
072		7	_aMMG _2bicssc
072		7	_aMED071000 _2bisacsh
082	0	4	_a615 _223
100	1		_aGieschke, Ronald. _eauthor.
245	1	0	_aDevelopment of Innovative Drugs via Modeling with MATLAB _h[electronic resource] : _bA Practical Guide / _cby Ronald Gieschke, Daniel Serafin.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2014.
300			_aXV, 399 p. 192 illus., 112 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aBackground of pharmacologic modeling -- First example of a computational model -- Differential equations in MATLAB -- Pharmacologic modeling -- Drug-disease modeling -- Population analyses -- Clinical trial simulation -- Graphics-based modeling -- Outlook -- Appendix A: Hints to MATLAB programs -- Appendix B: Solution to exercises.
520			_aThe development of innovative drugs is becoming more difficult while relying on empirical approaches. This inspired all major pharmaceutical companies to pursue alternative model-based paradigms. The key question is: How to find innovative compounds and, subsequently, appropriate dosage regimens? Written from the industry perspective and based on many years of experience, this book offers: §  Concepts for creation of drug-disease models, introduced and supplemented with extensive MATLAB programs §  Guidance for exploration and modification of these programs to enhance the understanding of key principles §  Usage of differential equations to pharmacokinetic, pharmacodynamic and (patho-) physiologic problems thereby acknowledging their dynamic nature §  A range of topics from single exponential decay to adaptive dosing, from single subject exploration to clinical trial simulation, and from empirical to mechanistic disease modeling. Students with an undergraduate mathematical background or equivalent education, interest in life sciences and skills in a high-level programming language such as MATLAB, are encouraged to engage in model-based pharmaceutical research and development.
650		0	_aMedicine.
650		0	_aToxicology.
650		0	_aPharmaceutical technology.
650		0	_aComputer simulation.
650		0	_aBiology _xData processing.
650	1	4	_aBiomedicine.
650	2	4	_aPharmacology/Toxicology.
650	2	4	_aPharmaceutical Sciences/Technology.
650	2	4	_aSimulation and Modeling.
650	2	4	_aComputer Appl. in Life Sciences.
700	1		_aSerafin, Daniel. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642397646
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-39765-3
912			_aZDB-2-SBL
999			_c93359 _d93359