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15.5 Drug Development
287
Currently, the application of AI in drug development has received considerable attention, so much so that researchers and drug companies are committing a lot of resources and effort to discover new probes or drugs for diagnostic or therapeutic purposes. This has cut down the time signicantly in discovering new drugs. AI can screen millions of drugs for specic diseases and design one or more that are most useful for diagnosis or therapy at a limited cost. Drug–target interaction can be pre­dicted by AI.AI-designed drugs are presumably more efcacious and safer for human use.
One advantage of the AI application in drug discovery is the availability of a massive amount of data in this eld, which is a prime requirement for the imple­mentation of AI.These datasets have been used to carry out AI methods in drug discovery. Ml, DL, GenAI, and many other AI paradigms have been employed. Blanco-Gonzalez etal. (2023) reported a comprehensive review of AI applications in drug discovery, addressing important issues of synthesis, drug interaction, repur­posing of approved drugs, etc. The authors took the help of ChatGPT 3.5 for orga­nizing the materials in the review, which was later reorganized with human input. Similarly, ChatGPT 3.5 was asked for a brieng on the AI application in drug dis­covery with some appropriate comments. Below are the essential points of the report provided by ChatGPT 3.5.
The rst step in the drug discovery is the identication of a target, namely, a pro­tein, gene, etc., causing the disease. AI can analyze the large datasets and nd the appropriate molecule as the target. The next step is to nd a drug molecule that binds strongly with the target molecule, which AI (machine learning) can help by shing through the vast datasets that contain millions of molecules to nd an effective one. GAN has been used to design De Novo (new) drug molecules specic for certain diseases. AI has the unique capability of predicting efcacy, toxicity, and side effects of a drug molecule. AI models can be trained to learn the pattern of these parameters in the datasets and accordingly help decide to accept or reject the drug molecule.
AI can be very helpful to organize a clinical trial of a new drug by recruiting the appropriate cohort of patients, by predicting an ideal dosage, and monitoring the protocol at every step of the study. Repurposing of a drug (use of an approved drug for a new disease) is a unique feature offered by AI through scrutinizing the massive dataset that is beyond the capacity of human comprehension. Research and develop­ment, automation in manufacturing, and marketing of the drug all can be performed with appropriate AI models. AI can furnish all amenities and guidance in fullling the regulatory requirements.
A few drugs have been discovered with the help of the AI application. When prompted, ChatGPT 3.5 provided the following list of drugs AI helped design (Table15.1).
288
15 Application ofArticial Intelligence inNuclear Medicine
Table 15.1
Drug/ compound
DSP-1181 Ex Scientia OCD Molecule design Phase I (2020) INS018_055 Insilico Medicine IPF Target + molecule Phase II (2023) Abaucin MIT/McMaster Bacterial
Halicin MIT Antibiotic Molecule
ISM001–055 Insilico Medicine Fibrotic diseases Full pipeline Early trials Baricitinib Benevolent AI COVID-19 Drug repurposing Approved use
a
Provided by ChatGPT 3.5, when prompted with a query: List of drugs AI helped design
Drugs made with the help of AI
Company/ institution Disease targeted AI role
a
infection
Molecule discovery
discovery
Development stage
Preclinical
Preclinical

15.6 Questions

1. Describe how articial intelligence (AI) is applied to nuclear medicine
operations.
2. Elucidate difculties encountered in the analysis of scan images by AI.
3. Explain how AI helps in image reconstruction in nuclear medicine.
4. Explain how attenuation and scatter corrections are made using AI.
5. Can ChatGPT help in diagnosing human diseases, and if so, how?
6. Describe how AI can guide you through to develop new drugs.
7. What are the advantages and disadvantages of ChatGPT in nuclear medicine?

References and Suggested Reading

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39070149; PMCID: PMC11272524. Blanco-González A, Cabezón A, Seco-González A , etal. The Role of AI in Drug Discovery:
Challenges, Opportunities, and Strategies. Pharmaceuticals (Basel). 2023; 16(6):891. https://
doi.org/10.3390/ph16060891.
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assessment of the myocardial perfusion in [
(2024). Buvat I and Weber W. Nuclear Medicine from a Novel Perspective : Buvat and Weber Talk with
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appointment delays. J Am Coll Radiol. 2018; 15:1310–6. Gandhi Z, Gurram P, Amgai B, et al. Articial Intelligence and Lung Cancer: Impact on
Improving Patient Outcome. Cancers (Basel). 2023;15(21):5236. https://doi.org/10.3390/can-
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of- Flight 18F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously
Reconstructed Activity and Attenuation Maps. J Nucl Med. 2019; 60(8):1183–1189. doi:
https://doi.org/10.2967/jnumed.118.219493.
Liu J, Cundy T, Woon DTS, et al. A Systematic Review on Articial Intelligence Evaluating
Metastatic Prostatic Cancer and Lymph Nodes on PSMA PET Scans. Cancer (Basel). 2024;
16(3): 486. https://doi.org/10.3390/cancers16030486. PMCID: PMC10854940 PMID:
38339239.
McMillan AB and Bradshaw TJ.AI-based data corrections for attenuation and scatter in PET and
SPECT.PET Clin. 2021 Aug 5; 16(4):543–552. https://doi.org/10.1016/j.cpet.2021.06.010. Milgrom SA, Elhalawan H, Lee J. etal. A PET radiomics model to predict refractory mediastinal
Hodgkin lymphoma. Sci Rep. 2019; 9: 1322. Nakajo M, Hirahara D, Jinguj M etal. Machine learning approach using
features and the visibility of right ventricle
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F-FDG uptake for predicting clinical events in
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F-FDG-PET-radiomic
patients with cardiac sarcoidosis · Jap J of Radiol 2024; 42:744–752. https://doi.org/10.1007/
s11604- 024- 01546- y.
Nensa F, Demircioglua A, Rischpler C.Articial Intelligence in Nuclear Medicine. J Nucl Med.
2019; 60:29s–37s. Ou X, Zhang J, Wang J, etal.. Radiomics based on
18
F-FDG PET/CT could differentiate breast car­cinoma from breast lymphoma using machine-learning approach: A preliminary study. Cancer Med. 2020; 9(2):496–506. https://doi.org/10.1002/cam4.2711. Epub 2019 Nov 25. PMID: 31769230; PMCID: PMC6970046.
Popescu C, Laudicella R, Baldari S et al. PET-based articial intelligence applications in car-
diac nuclear medicine. Swiss Medical Weekly. 2022; 152: w 30123. https://doi.org/10.4414/
SMW.2022.w30123.
Reader AJ and Schramm G. Articial Intelligence for PET Image Reconstruction. J Nucl Med.
2021; 62: 1330–1333.
Rogasch JMM, Jochens HV, Metzger G. et al. Keeping Up With ChatGPT. Evaluating Its
Recognition and Interpretation of Nuclear Medicine Images. Clinical Nuclear Medicine 2024: 49:500–504. https://doi.org/10.1097/RLU.0000000000005207.
Shiri I, Arabi H, Geramifar P, etal. Deep-JASC: joint attenuation and scatter correction in whole-
body 18F-FDG PET using a deep residual network. Eur J Nucl Med Mol Imaging. 2020; 47(11):2533–2548.
https://doi.org/10.1007/s00259- 020- 04852- 5.
Sollini M, Cozzi L, Ninatti G, Antunovic L, Cavinato L, Chiti A, Kirienko M.PET/CT radiomics
in breast cancer: Mind the step. Methods. 2021 Apr;188:122–132. https://doi.org/10.1016/j.
ymeth.2020.01.007. Epub 2020 Jan 21. PMID: P 31978538.
Srinivas S, Ravindran A R.Optimizing outpatient appointment system using machine learning
algorithms and scheduling rules: a prescriptive analytics framework. Expert Syst Appl. 2018; 102:245–261.
Trägårdh E, Enqvist O, Ulén J, etal.Freely available, fully automated ai-based analysis of primary
tumour and metastases of prostate cancer in whole-body
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F-psma-1007 pet-ct. Diagnostics.
2022;12:2101. https://doi.org/10.3390/diagnostics12092101.
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Wang F, Xu W, Lv W, et al. Evaluation of the diagnostic value of joint PET myocardial perfusion
and metabolic imaging for vascular stenosis in patients with obstructive coronary artery dis­ease. J Nucl Cardiol. 2020. https://doi.org/10.1007/s12350-020-02160-x.
Yang P, Pi Y, He T, etal. Automatic Differentiation of Thyroid Scintigram by Deep Convolutional
Neural Network: A Dual Center Study. BMC Med. Imaging. 2021;21:1–9. https://doi.
org/10.1186/s12880- 021- 00710- 4.
Zhao H, Su Y, Lyu Z, et al. Non-invasively Discriminating the Pathological Subtypes of Non-
small Cell Lung Cancer with Pretreatment 18F-FDG PET/CT Using Deep Learning. Academic Radiol. 2024; 31: 35–46. https://doi.org/10.1016/j.acra.2023.03.032.
Zhao, Z., Pi, Y., Jiang, L. etal. Deep neural network based articial intelligence assisted diag-
nosis of bone scintigraphy for cancer bone metastasis. Sci Rep 10, 17046 (2020). https://doi.
org/10.1038/s41598- 020- 74135.
15 Application ofArticial Intelligence inNuclear Medicine
4
RCkg=×
−
./

Internal Radiation Dosimetry

16
Radiation can cause detrimental effects on human tissues, and these effects depend on various factors, such as dose, dose rate, time of exposure, and so on. This chapter describes the method of calculating absorbed doses in various organs from radionu­clides ingested internally either purposely (e.g., medical procedures) or accidentally.

16.1 Radiation Unit

Three units of measure are related to radiation: the roentgen (R) for exposure, the rad (radiation absorbed dose) for absorbed dose, and the rem (roentgen equivalent man) for dose equivalent.

16.1.1 Roentgen

The roentgen is the amount of x- or γ-radiation that produces ionization of one elec- trostatic unit of either positive or negative charge per cubic centimeter of air at 0°C and 760 mm Hg, standard temperature and pressure (STP). Because 1 cm3 air weighs 0.001293 g at STP, and a charge of either sign carries 1.6× 10
4.8×10
It should be noted that the roentgen applies only to air and to x- or γ-radiations.
Because of practical limitations of the measuring instruments, the R unit is appli­cable only to photons of less than 3MeV energy.
© The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2025 G. B. Saha, Physics and Radiobiology of Nuclear Medicine,
https://doi.org/10.1007/978-1-0716-4816-2_16
−10
electrostatic units, it can be shown that
−19
C or
(16.1)
291
292
2
radJkg=
-
/
= 1J kgabsorber/
4
RJkg in air=×
−
./
0 0096
Rrad
Gy
==.
.
16 Internal Radiation Dosimetry

16.1.2 Rad

The rad is a more universal unit. It is a measure of the energy deposited per unit mass of any material by any type of radiation. The rad is specically dened as
(16.2)
Since 1joule(J)=107ergs,
(16.3)
Another radiation unit is kerma (acronym for kinetic energy released in matter),
which is dened as the sum of initial kinetic energies of all charged particles liber­ated by uncharged ionizing radiation per unit mass of material. For all practical purposes, kerma and rad are identical.

16.1.3 Gray

In SI units, the gray (Gy) is the unit of radiation absorbed dose and kerma and is given by
(16.4)
(16.5)
It can be shown that the energy absorbed per kilogram of air due to an exposure
of 1 R is
Therefore,
or,
Note that for soft tissue,
The rad is not restricted by the type of radiation or absorber or by the energy or
intensity of the radiation. It should be understood that the rad is independent of the weight of the material. This means that a radiation dose of 1rad (0.01Gy) is always 1rad (0.01Gy) in 1, 2, or 10g of the material. However, the integral absorbed dose is given in units of gram-rad (g·rad or g·Gy) and calculated by multiplying the rad
rem rad RBE=x
rem rad=xW
r
e1
e5
16.1 Radiation Unit
293
(Gy) by the mass of material. For example, if the radiation dose to a body of 45g is 10rad (0.1Gy), then the integral radiation dose to the material is 450 g⋅rad (or
4.5g·Gy); however, the radiation dose is still 10rad (0.1Gy).

16.1.4 Rem

The dose equivalent unit, rem, has been developed to account for the differences in effectiveness of different types of radiation in causing biological damage. In radio­biology, the rem is dened as
(16.6)
where RBE is the relative biological effectiveness of the radiation. It is dened as the ratio of the dose of a standard radiation to produce a particular biological response to the dose of the radiation in question to produce the same biological response. Radiations of 250 KV x-rays are normally chosen as the standard radia­tion because of their widespread use. RBE varies with the linear energy transfer (LET) of the radiation, radiation dose, dose rate, and the biological system in which RBE is determined.

16.1.5 Radiation Weighting Factor

In radiation protection, RBE is replaced by the radiation weighting factor, Wr, to account for differences in effectiveness of various radiations in causing biological damage. The rem is then dened as
(16.7)
The International Commission on Radiological Protection has suggested the Wr
values for different radiations, which are listed in Table16.1 (ICRP 103, 2007). These values depend on the LET of the radiation. When a radiation dose comes from several radiations, the total dose equivalent is calculated by adding the absorbed doses from individual radiations, multiplied by the W
ln 2/6,
−
En

()
2.5 +18.2
| |
5.0 +17.0 e
W
=
r
| |
2.5 + 3.25
ln 22/6
− ,,
En
()
ln 0.04 2/6,
En−
()
En
1MeV 50 MeV
En
≤≤
En
of each radiation.
r
MeV
<
0MeV
>
(16.8)
294
()
=
16 Internal Radiation Dosimetry
Table 16.1
Radiation type Radiation weighting factor, W Photons 1 Electrons and muons 1 Protons and charged pions 2 Alpha particles, ssion fragments,
heavy ions Neutrons A continuous curve as a function of neutron energy
a
All values relate to the radiation incident on the body or, for internal sources, emitted from the source Used with permission of Elsevier from ICRP Publication 103, Annals of the ICRP, vol 37: Nos 2–4; 2007: permission conveyed through Copyright Clearance Center, Inc.
Table 16.2
for different radiations
Radiation weighting factors W
Quality factors
a
in 2007 Recommendations, ICRP 103
r
r
20
Eq. (16.8)
Type of radiation QF X-rays, γ-rays, β-particles 1.0 Neutrons of unknown energy and
high-energy protons α-Particles 20.0 Heavy ions 20.0
10.0

16.1.6 Quality Factor

In the past, the US Nuclear Regulatory Commission (NRC) used the term quality factors (QF) for radiation weighting factors, which are somewhat different from the Wr values. The NRC still adopts these values for regulatory purposes, and the values
are listed in Table16.2.

16.1.7 Sievert

In SI units, the dose equivalent is expressed in sievert, which is dened as
In practical situations, all these radiation units are often expressed in milliroent­gens (mR), millirads (mrad), and millirems (mrem), which are 10−3 times the units, roentgen, rad, and rem, respectively. In SI units, the equivalent quantities are milli­grays (mGy) and millisieverts (mSv). A rad is also commonly expressed as centi­gray (cGy), one-hundredth of a gray.
(16.9)
ii
()=()()
()
()
ii
()=()←()

16.2 Dose Calculation

295
16.2 Dose Calculation
The radiation absorbed dose depends on a number of factors: (1) the amount of radioactivity administered; (2) the physical and biological half-lives of the radioac­tivity; (3) the fractional abundance of the radiation in question from the radionu­clides; (4) the biodistribution of radioactivity in the body; and (5) the fraction of energy released from the source organ that is absorbed in the target volume, which is related to the shape, composition, and location of the target. The physical charac­teristics of a radionuclide are well established. Information concerning the biodis­tribution of ingested radioactivity can be obtained from various experimental studies in humans and animals. Factors four and ve are variable from one individual to another and, therefore, they are approximated for a “standard” or “average” 70-kg man.
Radiopharmaceuticals administered to patients are distributed in different regions of the body. A region of interest for which the absorbed dose is to be calculated is considered the “target,” whereas all other regions contributing to the radiation dose to the target are considered “sources.” The source and the target become the same when the radiation dose due to the radioactivity in the target itself is calculated.

16.2.1 Radiation Dose Rate

Suppose a source volume r contains A μCi of a radiopharmaceutical emitting sev­eral radiations. If the ith radiation has energy Ei and a fractional abundance Ni per disintegration, then the energy absorbed per hour (dose rate) by a target of mass m and volume v from the ith radiation emitted by the source volume r is given by
RAmNE
radh Ci gMeV disintegration
// //
=
µ
4
.
××
37 10
-
./
××
16 10
grad erg
./
×.
001
()
×
./
213 AmNE
s
//
3600
()
()
disint
6
ergMeV
h
ii
i
eegrations
/s.
µ
Ci
The above equation is valid for nonpenetrating radiations only, meaning all energy is absorbed in the absorber. For penetrating radiations, total or part of the radiation energy may be absorbed in the absorbing material. If the target and the source are not the same, then a factor must be introduced to account for the partial absorption, if any, of the radiation energy. Thus,
RAmNEvr
radh/./
213
φ
ii
(16.10)
296
∆
iii
NE= 213.
mv
ii
()
()
()
mv
()
()
()
16 Internal Radiation Dosimetry
Here ϕi(v←r) is called the absorbed fraction and is dened as the ratio of the energy absorbed by the target volume v from the ith radiation to the energy emitted by the ith radiation from the source volume r. This is a critical factor that is difcult to evaluate, because the absorbed fraction ϕi depends on the type and energy of the radiation, the shape and size of the source Ἳvolume, and the shape, composition, and distance of the target volume. However, in the case of β-particles, conversion electrons, α-particles, and x- and γ-rays of energies less than 11 keV, all of the energy emitted by a radionuclide is absorbed in the volume r larger than 1cm. Then, ϕi becomes 0, unless v and r are the same, in which case ϕi=1. For x- and γ-rays with energies greater than 11keV, the value of decreases with increasing energy and varies between 0 and 1, depending on the energy. The values of ϕi are calculated by statistical Monte Carlo methods on the basis of fundamental mechanisms of interac­tion of radiation with matter, and are available in standard textbooks on radiation dosimetry, particularly the medical internal radiation dose (MIRD) pamphlets pub­lished by the Society of Nuclear Medicine.
The quantity 2.13 NiEi is a constant for the ith radiation and is often denoted by
Δi. Thus,
(16.11)
The quantity Δi is called the equilibrium dose constant for the ith radiation and has the unit g·rad/(μCi·h) based on the units chosen in Eq. (16.11). It should be pointed out that since β-particles are emitted with a distribution of energy, the aver­age energy E−β of β-particles, which is equal to one-third of E
of the particle, is
max
used in the calculation of Δi. Thus, Eq. (16.11) becomes
RA
radh//
=
∆
φ
r
←
i
(16.12)
The activity A will change due to the physical decay and biological elimination of the radiopharmaceutical, and therefore, the dose rate will also change. If Ao is the initial administered activity, then the activity localized in an organ is a fraction f of
. Assuming an effective exponential change in A with time, Eq. (16.12) can be
A
o
written as
t
−
λ
RfAe
radh/./
i
=
e
0
∆
ii
φ
←
r
(16.13)
Here λe is the effective decay constant of the radiopharmaceutical, and t is the time over which the original activity has decayed.

16.2.2 Cumulative Radiation Dose

The cumulative radiation dose Di to the target due to the ith radiation of the radio­nuclide during the period t=0 to t can be obtained by integrating Eq. (16.13). Thus,