Pre-clinical Models for Geriatric Pharmacotherapy

Abstract

With ageing of the population worldwide and discovery of new medications for prevention and management of age-related conditions, there is increasing use of medications by older adults. There are international efforts to increase the representativeness of participants in clinical trials to match the intended real-world users of the medications across a range of characteristics including age, multimorbidity, polypharmacy and frailty. Currently, much of the data on medication-related harm in older adults are from pharmacovigilance studies. New methods in pre-clinical models have allowed for measurement of exposures (such as chronic exposure, polypharmacy and deprescribing) and outcomes (such as health span functional measures and frailty) that are highly relevant to geriatric pharmacotherapy. Here we describe opportunities for design and implementation of pre-clinical models that can better predict drug effects in geriatric patients. This could improve the translation of new drugs from bench to bedside and improve outcomes of pharmacotherapy in older adults.

Full text

Vol.:(0123456789)
Drugs & Aging
https://doi.org/10.1007/s40266-024-01129-6
CURRENT OPINION
Pre‑clinical Models forGeriatric Pharmacotherapy
SarahN.Hilmer1 · KristinaJohnell2 · JohnMach1
Accepted: 16 June 2024
© The Author(s) 2024
Abstract
With ageing of the population worldwide and discovery of new medications for prevention and management of age-related
conditions, there is increasing use of medications by older adults. There are international efforts to increase the representative-
ness of participants in clinical trials to match the intended real-world users of the medications across a range of characteristics
including age, multimorbidity, polypharmacy and frailty. Currently, much of the data on medication-related harm in older
adults are from pharmacovigilance studies. New methods in pre-clinical models have allowed for measurement of exposures
(such as chronic exposure, polypharmacy and deprescribing) and outcomes (such as health span functional measures and
frailty) that are highly relevant to geriatric pharmacotherapy. Here we describe opportunities for design and implementation
of pre-clinical models that can better predict drug effects in geriatric patients. This could improve the translation of new
drugs from bench to bedside and improve outcomes of pharmacotherapy in older adults.
Key Points
1. New methods in pre-clinical models have allowed for
measurement of exposures (such as chronic exposure,
polypharmacy and deprescribing) and outcomes (such
as health span, functional measures and frailty) that are
highly relevant to geriatric pharmacotherapy.
2. These pre-clinical models may better predict drug
effects in geriatric patients, in whom medication use
is highly prevalent and who are vulnerable to adverse
effects.
3. While they are currently costly and time-consuming,
there is potential for these pre-clinical models to improve
the clinical translation of new drugs and the outcomes of
pharmacotherapy in older adults.
1 Why are Pre‑clinical Models Needed
forGeriatric Pharmacotherapy?
With ageing of the population worldwide comes increasing
use of medications by older adults.
Older adults are the most frequent users of multiple medi-
cations (polypharmacy) and are at high risk of adverse drug
events [1–3] . Application of pre-clinical models (includ-
ing cell culture, animal models and in silico models) that
are more relevant to older adults with polypharmacy and
multimorbidity will increase the translation of drug develop-
ment to clinical benefit for older adults in clinical trials and
clinical practice.
The increased use of medications imposes great chal-
lenges for our healthcare systems [4] . At the individual
level, the challenge is to individualise treatment to balance
risks related to drug treatment without denying older people
valuable drug therapy. At the societal level, the challenge is
to reduce drug-related ill health, hospitalisations and associ-
ated costs [5] .
Drug treatment in old age is complicated by concomitant
use of multiple medications (polypharmacy) [1] . Older indi-
viduals often have multiple diseases and impairments (e.g.
cardiovascular disease, musculoskeletal disease, psychiatric
disorders and cognitive impairment) [6] and therefore use
multiple drugs. There is also continuous development of new
drugs for age-related diseases. Polypharmacy is very com-
mon in old age; about half of the population aged ≥65years
* Sarah N. Hilmer
sarah.hilmer@sydney.edu.au
1 Kolling Institute, The University ofSydney andNorthern
Sydney Local Health District, StLeonards, NSW, Australia
2 Department ofMedical Epidemiology andBiostatistics,
Karolinska Institutet, Stockholm, Sweden

S.N.Hilmer et al.
is exposed (usually defined as concurrent use of ≥5 medi-
cations) [7, 8]. The net effects of polypharmacy are at pre-
sent impossible to foresee in an individual patient. There is
limited evidence of the effects of polypharmacy from ran-
domised clinical trials (RCTs), beyond occasional post hoc
subgroup analyses [9]. Polypharmacy can lead to unforeseen
effects and drug–drug interactions [10], particularly in the
context of age-related worsening of renal and hepatic func-
tion. [11] This can result in severe adverse outcomes, such
as falls [12] and cognitive [10] and functional decline [13].
These are sometimes misattributed to primary disease and
can lead to a prescribing cascade [14]. Polypharmacy has
increased over time [15, 16] and imposes considerable chal-
lenges for the individual patient, the prescriber and for the
society at large.
Adverse drug events contribute to up to 30% of hospital
admissions for older people [2] , and they entail immense
costs for healthcare systems [5] . Older patients are more
susceptible to adverse effects of medications than younger
adults [3] . Ageing leads to changes in pharmacokinetics and
pharmacodynamics, which result in prolonged and increased
effects of many drugs. Individual variation in drug response
and side effects is large and difficult to predict [2, 17].
Despite this complexity, the majority of knowledge
about medications is from monotherapy RCTs in younger
and healthier adults [18], which is of limited applicability to
older patients [19, 20]. RCTs are performed in a controlled
environment, and typically exclude patients at advanced
ages with polypharmacy, frailty and multimorbidity [19,
20]. Exclusion criteria for most RCTs include multiple dis-
eases and polypharmacy, which in practice excludes many
older patients. RCTs also typically investigate one drug for
one disease at a time, which rarely reflects the complexity
of drug treatment in old age. This together complicates the
translation of RCT results to the treatment of older patients
[19].
There are, however, international efforts to increase the
representativeness of participants in clinical trials to match
the intended real-world users of the medications across a
range of characteristics including age, multimorbidity,
polypharmacy and frailty [21]. A central concept in ageing
research is frailty because it strongly relates to advanced
age and adverse health among older adults [22]. Frailty
should thus be assessed in RCTs despite the inherent diffi-
culties [21]. Medication agencies and other regulatory bod-
ies should make efforts to increase numbers of frail older
adults in RCTs. Post-marketing and epidemiological studies
also need to include data on frailty to be able to provide
meaningful information from the real-world setting [21].
Furthermore, RCTs need to focus on outcomes that have
the highest value to older adults, such as function and inde-
pendence [21].
Currently, much of the data on medication-related harm
in older adults are from epidemiological and pharmacovigi-
lance studies. These real-world studies based on older people
with multimorbidity, polypharmacy and frailty will continue
to be important for geriatric pharmacotherapy [23]. How-
ever, these studies are often limited by small or selected
samples as well as bias and other methodological shortcom-
ings. The main challenge is to account for confounding by
multiple indications (confounding by multimorbidity and
frailty) [24], which means separating the potentially negative
effect of polypharmacy from the effect of multiple diseases
[15]. There is a need for translational research where large
epidemiological data and pre-clinical models in concert help
to inform about the underlying mechanisms and effects of
pharmacotherapy in old age [25].
A vulnerable geriatric group is people living with demen-
tia, who are particularly susceptible to adverse effects of
drugs [26]. The pathological brain changes causal to demen-
tia (e.g. a degenerative process such as Alzheimer’s disease
or micro- or macro-vascular compromise as an end-organ
effect of cardiovascular disease) lead to increased suscepti-
bility to many drugs [27]. Cognitive deficits and communi-
cation problems further complicate the drug treatment and
detection of side effects. However, few large studies have
assessed the quality of prescribing in people with demen-
tia [26]. Patients with dementia are typically excluded from
RCTs and informed consent can be difficult to obtain. These
patients are, however, likely medication users in the real-
world setting [28]. Even RCTs of anti-dementia drugs do
not adequately reflect the real-world setting of patients with
dementia. Participants in these RCTs are often younger, with
less comorbidities, than real-world patients with demen-
tia [29, 30]. To overcome these challenges, translational
research including both human and pre-clinical models of
dementia can be informative where evidence would other-
wise be difficult to obtain. Furthermore, measures of drug-
induced cognitive impairment are important outcomes in
pre-clinical and clinical research.
The great complexity of geriatric pharmacotherapy ren-
ders great need for precision medicine [25, 31]. Clinicians
need tools for improved prediction of expected clinical
effects and risk of side effects in individual older patients
[25, 32]. For a given patient, the challenge is to individu-
alise treatment to minimise risks of drug treatment without
denying older people valuable drug therapy. Artificial intel-
ligence and machine learning have the potential to handle
the complexity and variability in data from older adults. This
new technology could help create these highly needed tools
for optimising and tailoring drug treatment for the individual
patient [21]. These techniques can also use real-world data
to improve design of RCT eligibility criteria for representa-
tive recruitment. They can combine pre-clinical, RCT and

Geriatric Pharmacotherapy: Pre-clinical Models
real-world data for predictive analysis of adverse drug events
and drug–drug interactions [21, 33].
2 Could Pre‑clinical Models Provide More
Translatable Evidence forGeriatric
Pharmacotherapy?
Preclinical testing in the presence of geriatric changes could
help detect the effects of ageing on drug toxicity, as well as
on pharmacokinetics and efficacy. This information could
inform successful translation to the treatment of older adults.
Current guidance for pre-clinical testing of new drugs
deliberately excludes evaluation in old age. The Organisa-
tion for Economic Co-operation and Development (OECD)
guideline for chronic toxicity studies recommends a duration
of exposure that is long enough to allow any effects of cumu-
lative toxicity to become manifest, without the confounding
effects of geriatric changes [34]. Generally, these studies are
performed with chronic oral dosing in rodents for 12 months,
which is approximately equivalent to 30 human years for a
rat and 40 human years for a mouse. While this drug expo-
sure models the duration of many chronic therapies well,
clinically,mostchronic therapies are given in the presence
of geriatric changes in the second half of the lifespan, not
the first half.
New methods and models provide opportunities to test
drugs preclinically in more clinically relevant models. Medi-
cations that are likely to be used by significant numbers of
older adults should be tested in relevant pre-clinical models
to investigate age-related pharmacokinetic and pharmaco-
dynamic effects, analogous to the calls for representative
recruitment to clinical trials [35]. This is particularly impor-
tant for drugs that are hypothesised to have a positive or
negative impact on health span on in old age, affecting phys-
ical and cognitive function and frailty. Here we highlight
exposure characteristics, pre-clinical models and outcomes
that reflect clinical practice and clinical outcomes, which
should produce pre-clinical data more translatable to older
patients (Fig.1).
2.1 Exposures
Exposure to medications in pre-clinical models should
reflect the context of which they are used in humans for the
conditions of interest to increase clinical translation. This
includes the exposure dose, duration and exposure age (or
onset of morbidity). For example, people are usually diag-
nosed with heart failure with preserved ejection fraction in
old age and then commence chronic treatment. Therefore,
pre-clinical models should begin exposure in old age and
test exposure over periods equivalent to the years of clinical
treatment. Furthermore, drugs should be tested in the com-
binations in which they are used, including polypharmacy
for multimorbidity. This information can be derived from
cluster analyses of population data [36]. Preclinical models
should consider these variables to develop clinically relevant
models.
It is also important to evaluate the effects of stopping
chronic drug treatment, which is common practice in geri-
atric medicine patients when the ongoing benefits of treat-
ment no longer outweigh the risks, known as deprescribing
[37]. In the past decade, mouse models of polypharmacy
and deprescribing have been developed [38–45]. These
models demonstrated that old animals are more suscepti-
ble to polypharmacy-induced harm, chronic polypharmacy
and chronic monotherapy in old age can cause physical and
cognitive decline, deprescribing can reverse some outcomes
Fig. 1 Pre-clinical models for
geriatric pharmacotherapy

S.N.Hilmer et al.
and sex influences outcomes. Future studies are required to
explore mechanisms of these effects and investigate different
drug regimens and the impact of the multiple morbidities for
which the drugs are used. Pre-clinical studies can inform
deprescribing practice by investigating adverse drug with-
drawal effects from fast or gradual withdrawal, including the
risk of return of the underlying condition. These models of
polypharmacy and deprescribing could be adapted to pre-
clinical drug evaluation to ensure that the exposure matches
real-world exposures of older people.
2.2 Models
In pre-clinical drug development, the age ranges of the mod-
els should reflect the human conditions of interest and the
ages of people likely to use the drug clinically. Pre-clinical
models range from cell culture through to animals. Bioprint-
ing provides new opportunities to test drugs in models of
tissues that are structurally and functionally accurate [46],
with potential to replicate the physiology of ageing and the
pathologies of multimorbidity.
2.2.1 Cell culture
Cell culture is applied to conduct high throughput screen-
ing of drug effects [47]. In ageing research, cell culture
commonly involves primary culture of cells from aged ani-
mals. Whilst this provides opportunities to understand the
effects of drugs on individual cells and for conducting high
throughput screening for drug effects on age, it cannot test
the dynamic interplay occurring in the body in response
to drugs. Translatable clinical factors, including physical
function, disease, pain, withdrawal and tolerance, cannot be
evaluated in cell lines.
2.2.2 Animal models
Therefore, animal models are commonly employed to
measure complex multi-system effects on translatable clini-
cal outcomes. Ageing animals are the most common pre-
clinical model used to understand ageing and the effects of
interventions in and on ageing, mainly through longitudinal
studies or by studying animals of different age groups. Well-
characterised animal models of ageing range from yeast to
rodents to larger animals and non-human primates, which
offer different insights and translatability to humans [48].
Animal models of common age-related diseases are variably
translatable to disease in older adults, which often has com-
plex multifactorial pathogenesis. For example, a review of
models for vascular cognitive impairment induced through
brain lesions, reproducing risk factors (including ageing) or
genetic mutations in rodents and larger species, found that
no model replicated all pathologic and cognitive aspects of
human disease, and a deep understanding of each model
could guide selection for different experiments and inter-
pretation of results for translation [49].
Ageing is associated with multimorbidity. Typically,
drugs are evaluated in young animals with a single disease
for simplicity [50]. Ageing animals develop age-related
multimorbidity, with increasing prevalence of conditions
from osteoarthritis to renal impairment and/or cancer,
depending on the species studied. Some pre-clinical mod-
els induce morbidities and comorbidities relevant to the
drug being developed, informed by co-morbidity cluster
analyses in populations of older adults. This approach will
ensure that we understand drug-disease interactions, which
are highly relevant to multimorbid older patients. However,
to date there are limited studies on multimorbidity due to
the complexity of the models, labour intensity, significantly
increased cost and need for increased sample size to deal
with variability. Examples of comorbid pre-clinical studies
are described by Shabir etal. [51]. Work is needed to over-
come these challenges to ensure that preclinical findings are
applicable to older people.
Rodents are one of the most common species used to
investigate ageing and pharmacology due to their rela-
tively short lifespan and similarities to humans. Mice are
commonly investigated between the ages of 3–6months
for young mature adults (20–30 years human age),
10–14months for middle age (38–47years human age)
and 18–24months old age (56–69 years human years)
[52]. Interventions have variable effects in different mouse
strains. There are roles for studies in well-characterised
inbred strains to understand mechanisms of drug effects
and potentially reduce sample size in the absence of genetic
variability, as well as in outbred strains to improve gener-
alisability [53].
Investigating ageing in rodents is a lengthy task and
premature ageing models have been employed to reduce
experiment time [54]. These animals commonly display phe-
notypes in multiple organ systems that suggest premature
ageing and resemble features of natural ageing. These mod-
els provide insight into the molecular mechanisms involved
but generally represent rare premature ageing conditions in
humans, such as Hutchinson–Gilford progeria, Werner syn-
drome and Cockayne syndrome, limiting the generalisability
of the studies.
Other species and organisms have been investigated in
studies of ageing and pharmacology. Some, such as yeast,
fruit fly, fish and roundworm are simpler to conduct high
throughput testing, but they are limited by being simpler
organisms [55]. The obvious advantages of using these
models include that they are short-lived (compared with
humans), offer access to comprehensive resources such
as known genetic and transcriptomic data and have avail-
able experimental manipulation capabilities and extensive

Geriatric Pharmacotherapy: Pre-clinical Models
husbandry experience [56]. However, as these species are
different to humans and may have different evolutionary
toolboxes, this can lead to interpretation biases and inap-
propriate or false interpretations. In contrast, non-human
primates, which share >92% homology with humans, have
been used in ageing research and occasionally in drug devel-
opment [56]. However, their substantial size, cost, long lifes-
pan and stringent ethics requirements limit the use of this
model for drug evaluation unless scientifically essential.
2.3 Outcomes
To facilitate translation of drug evaluation from the bench
to bedside of geriatric patients, assessment of outcomes rel-
evant to older people are necessary. Over the past decade,
pre-clinical models of frailty have been developed on the
basis of the two main models used in humans: the frailty
index and the frailty phenotype, which can be applied to
drug evaluation [57]. As in clinical trials [21], frailty in
pre-clinical models can be applied at baseline to determine
whether drug effects vary with frailty [58], and as a clini-
cally important outcome measure. The toolbox for longitu-
dinal assessment of health span in ageing mice was proposed
in 2020 [59], which consists of clinically relevant measure-
ments of function of several vital systems such as the car-
diovascular (echocardiography), cognitive (novel object
recognition), neuromuscular (grip strength, rotarod) and
metabolic (glucose tolerance test and insulin tolerance test,
body composition and energy expenditure) health. Novel
technologies, such as automated behavioural cages, now
allow us to explore natural laboratory animal activity, analo-
gous to continuous virtual monitoring in clinical trials [44].
Additionally, advances in machine learning have allowed for
the possibility of machine-vision-based analysis of record-
ings of animal behaviour, opening up opportunities for big
data analysis, including the application of this to mouse gait,
posture, grooming activity and predicting frailty based on
morphometric and gait features [60–62]. These techniques
can all be applied to pre-clinical evaluation of the effects of
new drugs on clinically important outcomes in older people.
3 How Could these Pre‑clinical Models
forGeriatric Pharmacotherapy be
Validated andImplemented?
Implementation of pre-clinical models for assessment of
geriatric therapeutics would need validation of the models,
proof of concept studies, comprehensive analysis of potential
benefits and harms, provision of incentives and regulatory
change.
As discussed above, pre-clinical models for ageing,
multi-morbidity, polypharmacy and deprescribing have been
validated. Models of important geriatric outcomes, such as
frailty and health span, have been established. However,
there are many ways to consider appropriate models and
exposures and to measure each of these outcomes. There is
a need to evaluate and select the best models, exposures and
outcomes for testing drug effects in old age. For example,
different drugs and polypharmacy regimens have differ-
ent impacts on the mouse frailty phenotype and the mouse
clinical frailty index [63]. In mice, as in humans [21], it is
not yet clear which frailty measure is most relevant to drug
evaluation. Models may also need to be tailored to specific
situations to capture the inter-individual variability of older
adults, making it difficult to find a consistent approach for
testing. For example, different regimens of background poly-
pharmacy are relevant to evaluation of drugs treating differ-
ent conditions. Proof of concept studies are needed to com-
prehensively evaluate a range of therapeutic drugs that have
already been translated to human therapeutics. Such studies
would determine the best pre-clinical models and measures
to predict outcomes that were subsequently observed in
clinical trial and real-world data from older adults.
Pre-clinical evaluation in models that are more relevant
to geriatric therapeutics has potential benefits and harms.
The potential benefits are identification of any efficacy or
safety issues in old age prior to clinical trials. The benefits of
using more translatable models may be less scientific waste
because pre-clinical findings more reliably predict the sub-
sequent clinical trial outcomes. Pre-clinical evaluation in
models that are more applicable to geriatric patients will
increase confidence that drugs are likely to be efficacious or
safe in this population, which may facilitate recruitment of
older adults to clinical trials. If efficacy or safety issues are
detected in pre-clinical models of old age, then they can be
investigated further to determine whether the drug should be
tested in older adults. This will enable personalised medi-
cine and reduce post-marketing warnings and withdrawals
for drugs that are found to be unsafe when used by older
adults in practice. The harms of pre-clinical evaluation in
these models are primarily cost and time. It is expensive and
time consuming to test chronic exposures of drugs in aged
animals [50]. Scientific challenges include cost pressure, less
availability of historical data (compared with young adult
mice) to guide scientists, logistical issues for animal hus-
bandry and stricter labour-intensive animal welfare checks
that are necessary for aged animals. The models themselves
are labour intensive, such as measurement of health span
or frailty and long-term administration of drugs as chronic
monotherapy or with background polypharmacy. The
increased inter-individual variability in old age may require
larger sample sizes to evaluate drug effects. All these factors
need to be considered in estimating the cost-effectiveness of
using more clinically relevant models to evaluate drugs for
geriatric pharmacotherapy.

S.N.Hilmer et al.
In addition, in silico models developed from combina-
tions of data from molecular data, preclinical longitudinal
studies and available clinical data will enable accurate high-
throughput models to predict the effects of drugs in geriatric
patients. There is a need to compute this knowledge in a
form that can be efficiently translated to clinical practice
[64]. Currently, in silico methods have been proposed as a
strategy to accelerate the performance of clinical trials tar-
geting human ageing [64] and to identify potential drugs that
modulate the ageing process [65]. Future research should
consider polypharmacy within these models, as this is the
context geroprotective agents will be applied in in most older
adults. Research into in silico models for ageing, polyphar-
macy, multimorbidity and frailty could provide insights for
geriatric pharmacotherapy practice.
Implementation of more clinically relevant pre-clinical
evaluation of geriatric therapeutics will require incentives.
These could include additional funding to test aged or frail
animals, analogous to the National Institutes of Health
(NIH) funding provided to encourage testing of male and
female animals. Ultimately, if these models are found to be
useful in improving the pipeline for efficacious and safe drug
use in older adults, the efficiency generated may be enough
to drive changes in pre-clinical drug evaluation. To ensure
consistency of approach, regulatory change to pre-clinical
evaluation requirements will be needed. This could include
models that are representative of the populations that will
use the drug, in terms of age, sex, exposure types and dura-
tions and morbidities, as well as measurement of important
outcomes for older adults such as frailty and health span.
4 Conclusions
Now, early in the United Nations Decade of Healthy Ageing,
there are great opportunities to improve pre-clinical drug
evaluation to ensure that new drugs help older people to
participate in and contribute to their communities and soci-
ety. Pre-clinical models for drug evaluation that are more
translatable to the older adults, who are major users of medi-
cations, can improve effectiveness and safety of medications
and inform precision medicine in the geriatric population.
The extent to which these pre-clinical studies will need to
be performed invivo versus in silico is an exciting future
frontier.
Declarations
Funding Open Access funding enabled and organized by CAUL and its
Member Institutions. J.M. is supported by the Penney Ageing Research
Unit, Royal North Shore Hospital, Australia. No specific funding was
received to support writing this opinion piece.
Conflict of Interest S.H., J.M. and K.J. all conduct research on pre-
clinical polypharmacy models.
Author Contributions S.H. conceptualised and co-ordinated this opin-
ion piece. All authors contributed opinions to the plan and contributed
to drafting sections of the manuscript. S.H. edited the manuscript and
all authors approved the submitted manuscript.
Availability of Data and Material Data sharing not applicable to this
article as no datasets were generated or analysed during the current
study.
Ethics Approval Not applicable.
Consent to Participate Not applicable.
Consent for Publication Not applicable.
Code Availability Not applicable.
Open Access This article is licensed under a Creative Commons Attri-
bution-NonCommercial 4.0 International License, which permits any
non-commercial use, sharing, adaptation, distribution and reproduction
in any medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other
third party material in this article are included in the article’s Creative
Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons
licence and your intended use is not permitted by statutory regula-
tion or exceeds the permitted use, you will need to obtain permission
directly from the copyright holder. To view a copy of this licence, visit
http://creativecommons.org/licenses/by-nc/4.0/.
References
1. Wastesson JW, Canudas-Romo V, Lindahl-Jacobsen R, Johnell
K. Remaining life expectancy with and without polypharmacy:
a register-based study of Swedes aged 65 years and older. J Am
Med Dir Assoc. 2016;17(1):31–5.
2. Salvi F, Marchetti A, D’Angelo F, Boemi M, Lattanzio F, Cheru-
bini A. Adverse drug events as a cause of hospitalization in older
adults. Drug Saf. 2012;35(Suppl 1):29–45.
3. Shehab N, Lovegrove MC, Geller AI, Rose KO, Weidle NJ, Bud-
nitz DS. US emergency department visits for outpatient adverse
drug events, 2013–2014. JAMA. 2016;316(20):2115–25.
4. Wastesson JW, Fastbom J, Johnell K. Expanding the proportion
of life with polypharmacy in Sweden: 2006–2013. J Am Med Dir
Assoc. 2016;17(10):957–8.
5. Budnitz DS, Lovegrove MC, Shehab N, Richards CL. Emergency
hospitalizations for adverse drug events in older Americans. N
Engl J Med. 2011;365(21):2002–12.
6. Calderon-Larranaga A, Vetrano DL, Onder G, Gimeno-Feliu
LA, Coscollar-Santaliestra C, Carfi A, etal. Assessing and
measuring chronic multimorbidity in the older population: a pro-
posal for its operationalization. J Gerontol A Biol Sci Med Sci.
2017;72(10):1417–23.
7. Johnell K, Fastbom J. Comparison of prescription drug use
between community-dwelling and institutionalized elderly in
Sweden. Drugs Aging. 2012;29(9):751–8.
8. Wastesson JW, Cedazo Minguez A, Fastbom J, Maioli S, Joh-
nell K. The composition of polypharmacy: a register-based study

Geriatric Pharmacotherapy: Pre-clinical Models
of Swedes aged 75 years and older. PLoS ONE. 2018;13(3):
e0194892.
9. Jaspers Focks J, Brouwer MA, Wojdyla DM, Thomas L, Lopes
RD, Washam JB, etal. Polypharmacy and effects of apixaban ver-
sus warfarin in patients with atrial fibrillation: post hoc analysis
of the ARISTOTLE trial. BMJ. 2016;353: i2868.
10. Trevisan C, Limongi F, Siviero P, Noale M, Cignarella A, Man-
zato E, etal. Mild polypharmacy and MCI progression in older
adults: the mediation effect of drug-drug interactions. Aging Clin
Exp Res. 2019;33:49–56.
11. Ernst R, Fischer K, de GoRezende Costa Molino C, Orav EJ,
Theiler R, Meyer U, etal. Polypharmacy and kidney function in
community-dwelling adults age 60 years and older: a prospective
observational study. J Am Med Dir Assoc. 2020;21(2):254–91.
12. Morin L, Calderon Larranaga A, Welmer AK, Rizzuto D, Wast-
esson JW, Johnell K. Polypharmacy and injurious falls in older
adults: a nationwide nested case-control study. Clin Epidemiol.
2019;11:483–93.
13. Katsimpris A, Linseisen J, Meisinger C, Volaklis K. The associa-
tion between polypharmacy and physical function in older adults:
a systematic review. J Gen Intern Med. 2019;34(9):1865–73.
14. Rochon PA, Gurwitz JH. The prescribing cascade revisited. Lan-
cet. 2017;389(10081):1778–80.
15. Wastesson JW, Morin L, Tan ECK, Johnell K. An update on the
clinical consequences of polypharmacy in older adults: a narrative
review. Expert Opin Drug Saf. 2018;17(12):1185–96.
16. Wastesson JW, Fritzell J, Burstrom B, Johnell K, Fastbom J.
Regional variations in excessive polypharmacy and potentially
inappropriate drug use among older adults in Sweden: trends from
2006 to 2020. Front Pharmacol. 2023;14:1030849.
17. Reeve E, Wiese MD, Mangoni AA. Alterations in drug dis-
position in older adults. Expert Opin Drug Metab Toxicol.
2015;11(4):491–508.
18. Cerreta F, Eichler HG, Rasi G. Drug policy for an aging popu-
lation—the European Medicines Agency’s geriatric medicines
strategy. N Engl J Med. 2012;367(21):1972–4.
19. Tinetti ME. The gap between clinical trials and the real world:
extrapolating treatment effects from younger to older adults.
JAMA Intern Med. 2014;174(3):397–8.
20. Watts G. Why the exclusion of older people from clinical research
must stop. BMJ. 2012;344: e3445.
21. Hilmer SN, Schwartz J, Petrovic M, Walker LE, Thurmann P,
Le Couteur DG (2024) Addressing the gaps in evaluation of new
drugs for older adults: strategies from the International Union of
Basic and Clinical Pharmacology (IUPHAR) Geriatric Commit-
tee. J Am Geriatr Soc
22. Liau SJ, Lalic S, Sluggett JK, Cesari M, Onder G, Vetrano DL,
etal. Medication management in frail older people: consensus
principles for clinical practice, research, and education. J Am Med
Direc Assoc. 2021;22(1):43–9.
23. Tan ECK, Sluggett JK, Johnell K, Onder G, Elseviers M, Morin
L, etal. Research priorities for optimizing geriatric pharma-
cotherapy: an international consensus. J Am Med Dir Assoc.
2018;19(3):193–9.
24. Zhang HT, McGrath LJ, Ellis AR, Wyss R, Lund JL, Stur-
mer T. Restriction of pharmacoepidemiologic cohorts to ini-
tiators of medications in unrelated preventive drug classes to
reduce confounding by frailty in older adults. Am J Epidemiol.
2019;188(7):1371–82.
25. Johnell K. The controversies surrounding polypharmacy
in old age—where are we? Expert Rev Clin Pharmacol.
2018;11(9):825–7.
26. Johnell K. Inappropriate drug use in people with cognitive impair-
ment and dementia: a systematic review. Curr Clin Pharmacol.
2015;10(3):178–84.
27. Bonfiglio V, Umegaki H, Kuzuya M. Potentially inappropriate
medications and polypharmacy: a study of older people with
mild cognitive impairment and mild dementia. J Alzheimers Dis.
2019;71(3):889–97.
28. Brenowitz WD, Yaffe K. Observational studies in Alzheimer
disease: bridging preclinical studies and clinical trials. Nat Rev
Neurol. 2022;18(12):747–57.
29. Banzi R, Camaioni P, Tettamanti M, Bertele V, Lucca U. Older
patients are still under-represented in clinical trials of Alzheimer’s
disease. Alzheimers Res Ther. 2016;8:32.
30. Leinonen A, Koponen M, Hartikainen S. Systematic review: rep-
resentativeness of participants in RCTs of acetylcholinesterase
inhibitors. PLoS ONE. 2015;10(5): e0124500.
31. Fujita K, Masnoon N, Mach J, O’Donnell LK, Hilmer SN. Poly-
pharmacy and precision medicine. Camb Prism Precis Med.
2023;1: e22.
32. Jennings E, Gallagher P, O’Mahony D. Detection and preven-
tion of adverse drug reactions in multi-morbid older patients. Age
Ageing. 2019;48(1):10–3.
33. Xu J, Zhang H, Zhang H, Bian J, Wang F. Machine learning ena-
bled subgroup analysis with real-world data to inform clinical trial
eligibility criteria design. Sci Rep. 2023;13(1):613.
34. OECD (2018) Test No. 452: Chronic Toxicity Studies 2018.
35. Liu Q, Schwartz JB, Slattum PW, Lau SWJ, Guinn D, Madabushi
R, etal. Roadmap to 2030 for drug evaluation in older adults. Clin
Pharmacol Ther. 2022;112(2):210–23.
36. Held F, Le Couteur DG, Blyth FM, Hirani V, Naganathan V,
Waite LM, etal. Polypharmacy in older adults: association rule
and frequent-set analysis to evaluate concomitant medication use.
Pharmacol Res. 2017;116:39–44.
37. Reeve E, Gnjidic D, Long J, Hilmer S. A systematic review of the
emerging de fi nition of “deprescribing” with network analysis:
implications for future research and clinical practice. Br J Clin
Pharmacol. 2015;80(6):1254–68.
38. Huizer-Pajkos A, Kane AE, Howlett SE, Mach J, Mitchell SJ, de
Cabo R, etal. Adverse geriatric outcomes secondary to polyphar-
macy in a mouse model: the influence of aging. J Gerontol A Biol
Sci Med Sci. 2016;71(5):571–7.
39. Mach J, Gemikonakli G, Logan C, Vander Wyk B, Allore H,
Ekambareshwar S, etal. Chronic polypharmacy with increasing
drug burden index exacerbates frailty and impairs physical func-
tion, with effects attenuated by deprescribing, in aged mice. J
Gerontol A Biol Sci Med Sci. 2021;76(6):1010–8.
40. Eroli F, Johnell K, Latorre Leal M, Adamo C, Hilmer S, Wastes-
son JW, etal. Chronic polypharmacy impairs explorative behav-
ior and reduces synaptic functions in young adult mice. Aging
(Albany NY). 2020;12(11):10147–61.
41. Eroli F, Johnell K, Latorre-Leal M, Hilmer S, Wastesson J,
Cedazo-Minguez A, etal. Long-term exposure to polypharmacy
impairs cognitive functions in young adult female mice. Aging
(Albany NY). 2021;13(11):14729–44.
42. Wu H, Mach J, Gemikonakli G, Tran T, Allore H, Gnjidic D, etal.
Polypharmacy results in functional impairment in mice: novel
insights into age and sex interactions. J Gerontol A Biol Sci Med
Sci. 2021;76(10):1748–56.
43. Gemikonakli G, Mach J, Tran T, Wu H, Hilmer S. Probing poly-
pharmacy, ageing and sex effects on physical function using dif-
ferent tests. Fundam Clin Pharmacol. 2024;38:561–74.
44. Tran T, Mach J, Gemikonakli G, Wu H, Allore H, Howlett SE,
etal. Diurnal effects of polypharmacy with high drug burden
index on physical activities over 23 h differ with age and sex. Sci
Rep. 2022;12(1):2168.
45. Gemikonakli G, Mach J, Zhang F, Bullock M, Tran T, El-Omar
E, etal. Polypharmacy with high drug burden index (DBI) alters
the gut microbiome overriding aging effects and is reversible with
deprescribing. J Geron Ser A Biol Sci Med Sci. 2022;78:213–22.

S.N.Hilmer et al.
46. Awad A, Trenfield SJ, Goyanes A, Gaisford S, Basit AW. Reshap-
ing drug development using 3D printing. Drug Discov Today.
2018;23(8):1547–55.
47. Ghanemi A. Cell cultures in drug development: applications, chal-
lenges and limitations. Saudi Pharm J. 2015;23(4):453–4.
48. Santulli G, Borras C, Bousquet J, Calza L, Cano A, Illario M, etal.
Models for preclinical studies in aging-related disorders: one is
not for all. Transl Med UniSa. 2015;13:4–12.
49. Hainsworth AH, Allan SM, Boltze J, Cunningham C, Far-
ris C, Head E, etal. Translational models for vascular cogni-
tive impairment: a review including larger species. BMC Med.
2017;15(1):16.
50. Jackson SJ, Andrews N, Ball D, Bellantuono I, Gray J, Hachoumi
L, etal. Does age matter? The impact of rodent age on study
outcomes. Lab Anim. 2017;51(2):160–9.
51. Shabir O, Moll TA, Matuszyk MM, Eyre B, Dake MD, Berwick
J, etal. Preclinical models of disease and multimorbidity with
focus upon cardiovascular disease and dementia. Mech Ageing
Dev. 2020;192: 111361.
52. Flurkey K, Currer J, Harrison D. The mouse in aging research.
Mouse Biomed Res. 2007;3:637–72.
53. Tuttle AH, Philip VM, Chesler EJ, Mogil JS. Comparing pheno-
typic variation between inbred and outbred mice. Nat Methods.
2018;15(12):994–6.
54. Harkema L, Youssef SA, de Bruin A. Pathology of mouse models
of accelerated aging. Vet Pathol. 2016;53(2):366–89.
55. Holtze S, Gorshkova E, Braude S, Cellerino A, Dammann
P, Hildebrandt TB, etal. Alternative animal models of aging
research. Front Mol Biosci. 2021;8: 660959.
56. Mattison JA, Vaughan KL. An overview of nonhuman primates
in aging research. Exp Gerontol. 2017;94:41–5.
57. Kane AE, Hilmer SN, Mach J, Mitchell SJ, de Cabo R, Howl-
ett SE. Animal models of frailty: current applications in clinical
research. Clin Interv Aging. 2016;11:1519–29.
58. Kane AE, Mitchell SJ, Mach J, Huizer-Pajkos A, McKenzie C,
Jones B, etal. Acetaminophen hepatotoxicity in mice: effect of
age, frailty and exposure type. Exp Gerontol. 2016;73:95–106.
59. Bellantuono I, de Cabo R, Ehninger D, Di Germanio C, Lawrie
A, Miller J, etal. A toolbox for the longitudinal assessment of
healthspan in aging mice. Nat Protoc. 2020;15(2):540–74.
60. Sheppard K, Gardin J, Sabnis GS, Peer A, Darrell M, Deats S,
etal. Stride-level analysis of mouse open field behavior using
deep-learning-based pose estimation. Cell Rep. 2022;38(2):
110231.
61. Hession LE, Sabnis GS, Churchill GA, Kumar V. A machine-
vision-based frailty index for mice. Nat Aging. 2022;2(8):756–66.
62. Geuther BQ, Peer A, He H, Sabnis G, Philip VM, Kumar V.
Action detection using a neural network elucidates the genetics
of mouse grooming behavior. Elife. 2021. https:// doi. org/ 10. 7554/
eLife. 63207.
63. Mach J, Allore H, Gnjidic D, Gemikonakli G, Kane AE, Howlett
SE, etal. Preclinical frailty assessments: phenotype and frailty
index identify frailty in different mice and are variably affected
by chronic medications. Exp Gerontol. 2022;161: 111700.
64. Menendez JA, Cuyas E, Folguera-Blasco N, Verdura S, Martin-
Castillo B, Joven J, etal. In silico clinical trials for anti-aging
therapies. Aging (Albany NY). 2019;11(16):6591–601.
65. Donertas HM, Fuentealba M, Partridge L, Thornton JM. Identi-
fying potential ageing-modulating drugs in silico. Trends Endo-
crinol Metab. 2019;30(2):118–31.