Furumoto S.

Nai YH.

Okamura N.

Seki C.

Shidahara M.

Tashiro M.

Watabe H.

Yanai K.

紀要類（bulletin）departmental bulletin pape

Shidahara, M.

Seki, C.

Nai, YH.

Okamura, N.

Furumoto, S.

Yanai, K.

Watabe, H.

Tashiro, M.

Tohoku University Repository (TOUR) / 東北大学機関リポジトリ

Biomathematical Modeling Approach to PredictClinical SUVRs for Amyloid PET Imaging著者 Shidahara M., Seki C., Nai YH., Okamura N.,Furumoto S., Yanai K., Watabe H., Tashiro M.journal orpublication titleCYRIC annual reportvolume 2016-2017page range 147-151year 2017URL http://hdl.handle.net/10097/00128081147  CYRIC Annual Report 2016-2017    VII. 5.  Biomathematical Modeling Approach to Predict Clinical SUVRs  for Amyloid PET Imaging    Shidahara M.1,2, Seki C.3, Nai YH.2, Okamura N.4, Furumoto S.2,  Yanai K.5, Watabe H.2, and Tashiro M.2  1Department of Quantum Science and Energy Engineering, Tohoku University 2Cyclotron Radioisotope Center, Tohoku University 3National Institutes for Quantum and Radiological Science and Technology 4Faculty of Medicine, Tohoku Medical and Pharmaceutical University 5Department of Pharmacology, Tohoku University School of Medicine    Introduction The aggregation of amyloid β peptide is one of the pathological observations in the brains of individuals with Alzheimer’s disease (AD). Amyloid imaging using positron emission tomography (PET) has been recognized as having an important role in the diagnosis of AD1). In the last decade, many PET radioligands for amyloid imaging have been developed; some of them have successfully been applied in human PET studies. In general, the discovery and development of radioligands for clinical application requires complicated and sometimes empirical procedures in terms of chemical (e.g., stability of labeling and lipophilicity) or biological (e.g., affinity, metabolites and density of the target) factors2,3). Even though these factors have been well-investigated in the case of candidate radioligands, several factors obtained in in vitro or in vivo animal studies may not be applicable to human studies; it is not easy to develop successful radioligands and satisfy clinical demands4). We need to know not only the micro parameters of the candidate radioligands but also overall macroscopic performance. Recently, there has been growing interest concerning more efficient development of successful radioligands in clinical studies using systematic evaluation of their overall performance (e.g., outcome measures)3). It is obvious that the use of in vivo PET scans in experimental animals or humans is the fastest and easiest approach for the evaluation of the overall performance of the candidate radioligand. However, the development of a labelling protocol for positron emitter isotopes and the synthesis of a radioligand for PET studies are 148  labor intensive and also take considerable time. Therefore, in order to contribute on the radioligand development, we proposed a new method to predict standardized uptake value (SUVR) of amyloid PET radioligands using biomathematical modeling and in silico parameters (Fig. 1)5). The methodology includes empirical formula of lipophilosity (logP), free fraction of radioligand in blood (fp) and free fraction of radioligand in tissue (fND). In this study, we investigated the influence of empirical formulae based on 3 datasets of fp and fND reported by Guo et al.3), Summerfield et al.6), and Wan&Mauer7,8) on the outcome, predicted SUVRs, of [11C]PiB, [11C]BF-227, [11C]AZD2184, [11C]SB-13, [18F]FACT, [18F]florbetapir, [18F]florbetaben, [18F]flutemetamol, [18F]FDDNP and [18F]AZD4694.  Material and Methods We assumed that the radioligand for amyloid imaging obeyed the simplified one-tissue compartment model (1TCM) (Fig. 1). The kinetic parameters (K1, k2 and BPND) for each radioligand in the human brain were mathematically modelled, where K1, k2 and BPND are the influx and efflux rate constants between arterial plasma and brain tissue, and the binding potential, respectively. The time–activity curves (TACs), with or without specific binding of the radioligand in brain tissue, were calculated as follows: ( ) ( )( ) ( ) )exp(1exp2121argtktCKtCtBPktCKtCpreferenceNDpett−⊗⋅=+−⊗⋅=                    (1) where Ctarget is the TAC of the target region, where the radioligand specifically binds to the target protein. Creference is the TAC of the reference region without the target protein. Cp is the arterial plasma input function. The influx rate constant from plasma to brain tissue, K1 [mL/cm3/min] was formulated using the Renkin–Crone model as follows:  ( )[ ]fPSefK /1 1 −−⋅=                            (2) where P, f and S are capillary permeability [cm/min], perfusion [mL/cm3/min] and capillary surface area [cm2/cm3 of brain], respectively. f and S were set to 0.6 [mL/cm3/min] and 150 [cm2/cm3 of brain], respectively. Permeability P in Eq. (2) was empirically formulated as follows 3): ( ) ( ){ }525.2log544.2298.2log121.0 3/1210 −−−−= xVDcP            (3) where clogD and Vx are lipophilicity and the McGowan volume [cm3/mol/100], respectively. The efflux rate constant from brain tissue to plasma, k2 [1/min], was expressed using the following equation, assuming equilibrium in the radioligand 149  concentrations between plasma and brain tissue. PNDTaqPaqffVKVk ⋅⋅=_1_2                               (4) where Vaq_p and Vaq_T are the apparent aqueous volume in plasma, the apparent aqueous volume in tissue and these were set to 0.98 [solvent/mL of plasma] and 0.9 [solvent/mL of tissue], respectively3). Both fP and fND were estimated from empirical formulae, which were established from fP and fND measured from in vitro binding experiments using mouse brain and plasma, and in silico lipophilicity. Here in this study, we tested 3 data sets of fP and fND reported by Guo et al.3), Summerfield et al.6), and Wan&Mauer7,8). The radioligand binding capacity at the target site, BPND, was modelled5) as follows: ( )4042404240404242 1−−−−−−−−+= −+=+=availavailavailavailDDNDDavailDavailNDNDBBBBKaKafKBKBfBP      (5)                              where a, KD-42 and KD-40 are the fractions of Aβ1-42, the dissociation constant for Aβ1-42 and Aβ1-40, respectively. Bavail-42, Bavail-40 and Bavail were available binding sites of Aβ1-42 and Aβ1-40, (Aβ1-42 + Aβ1-40), respectively. a was assumed to have a value of 0.7, which is biochemically derived from fractions of extra-cellular insoluble Aβ1-42 in both AD and HC brains 9). The TACs of the target and reference regions were calculated using K1, k2 and BPND and fixed arterial input function Cp using Eq. (1). The parameter of interest, SUVR, was then estimated from the predicted TACs. Finally, for each data sets of fp and fND, the predicted SUVR were compared with their clinical counterparts, SUVR5).   Results and discussion Figure 2A shows the relationships between in silico lipophilicity and in vitro fND, and Fig. 2B shows the relationships between in vitro fND and fP using the datasets of Guo’s, Summerfield’s and Wan&Maurer’s, respectively. For each dataset, correlations between lipophilicity and fND and between fND and fP were observed. Figure 3 shows the relationship between predicted SUVR and clinically observed SUVRs, these results were obtained by applying empirical formulae in Fig. 2 into the prediction scheme of SUVR of the 10 amyloid radioligands. In this study, we calculated Moriguchi logP as the lipophilicity5). Even though there was inconsistent use of lipophilicity between the 3 datasets (clogD and clogP by different softwares) and ours (MlogP) for the estimation of fp and fND, positive 150  correlations between predicted and clinically observed SUVRs were observed (Fig. 3). These results supported the fact that our approach (MlogP) without the use of in vitro experiments for fp and fND estimation can be applied to other amyloid radioligands, even though the data-sets selected for estimation of fp and fND resulted in different values of the predicted SUVRs.   Conclusion For all 3 data sets of fp and fND, predicted SUVR showed good correlation with clinically observed SUVR for the 10 clinically applied amyloid tracers, however, the values of predicted SUVR were different from each datasets. These results will be contributed on the future improvement of the methodology.   References  1) Okamura N, Harada R, et al. Ageing Res Rev.  (2016) 107-13. 2) Innis RB, Cunningham VJ, et al. J Cereb Blood Flow Metab. 27 (2007) 1533-9. 3) Guo Q, Brady M, et al. J Nucl Med. 50 (2009) 1715-23. 4) Laruelle M, Slifstein M, et al. Mol Imaging Biol. 5 (2003) 363-75. 5) Arakawa Y, Nai Y, et al. J Nucl Med. 58 (2017) 1285-92. 6) Summerfield SG, Read K, et al. J Pharmacol Exp Ther. 322 (2007) 205-13. 7) Maurer TS, Debartolo DB, et al. Drug metabolism and disposition: the biological fate of chemicals. 33 (2005) 175-81. 8) Wan H, Rehngren M, et al. J Med Chem. 50 (2007) 4606-15. 9) Steinerman JR, Irizarry M, et al. Arch Neurol. 65 (2008) 906-12.      Figure 1.  Overview of our biomathematical model: From physicochemical/biological properties of PET radioligand, pharmacokinetic parameters (K1, k2 and BPND) are estimated, then outcome measure (SUVR) is predicted through time activity curves of simplified 1 tissue model for both target and reference regions.   151   Figure 2 Relationship between in silico lippophilisity and fp (A) and between fp and fND (B) for 3 databases of Guo’s, Summerfield’s and Wan&Maurer’s.     Figure 3  Relationship between clinically observed SUVR and predicted SUVR by our model based on 3 databases, Guo’s (A), Summerfield’s (B) and Wan&Maurer’s (C). 

Biomathematical Modeling Approach to Predict Clinical SUVRs for Amyloid PET Imaging

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Biomathematical Modeling Approach to Predict Clinical SUVRs for Amyloid PET Imaging

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