xxxii
Fig. 9.1 Role RNAi in development of personalized medicine .................. 196
Fig. 10.1 Relationships of technologies for personalized
management of cancer ................................................................... 201
Fig. 10.2 Role of nanobiotechnology in personalized management
of cancer ......................................................................................... 256
Fig. 11.1 An integrated approach to viral diseases ....................................... 388
Fig. 11.2 Mode of action of some current anti-HIV drugs ............................ 393
Fig. 11.3 Workfl ow of genotypic resistance analysis for personalized
HIV therapy. Workfl ow of current-day genotypic resistance
analysis. The process begins by detecting the viral load ( 2 )
in a patient ( 1 ). In the case of anticipated therapy change
the viral genome is sequenced from the patient’s blood
serum ( 3 ). Interpretations of the viral genome sequence
is effected either manually using a mutation table (4a),
or via a rules-based system (4b), or with a statistical
model derived from clinical resistance data (4c).
The interpretation results in a resistance profi le ( 5 )
that is qualitative in the fi rst two cases and quantitative
when using statistical models. The physician uses
this profi le to select a therapy ( 9 ). In doing so, additional
information on the patient is also taken into account
(patient history, habits, drug side effects, etc. ( 6 ).
Therapy prediction engines ( 7 ) can assist this process
by a quantitative analysis that yields a list of therapies
ranked by their likelihood of success ( 8 )
(Source: Lengauer et al. 2014) ....................................................... 399
Fig. 12.1 Relationships of neurogenomics with other omics ........................ 410
Fig. 12.2 Role of neurogenomics in the development
of personalized neurology .............................................................. 411
Fig. 12.3 Scheme of iPSCs for personalized cell therapy
of Parkinson disease ....................................................................... 424
Fig. 12.4 An algorithm for personalized management of epilepsy ............... 434
Fig. 12.5 Essential components of personalized management
of pain ............................................................................................ 447
Fig. 12.6 Genetic & non-genetic factors affecting effi cacy
and side effects of opioids (Modifi ed from Sadhasivam
et al. 2014) ..................................................................................... 450
Fig. 12.7 An algorithm for personalized management of pain ...................... 453
Fig. 14.1 A scheme of personalized approach to management
of hypertension ............................................................................... 499
List of Figures