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First Fill

First Fill refers to the initial dose or application of a medication or treatment.
It is a critical step in the drug development and clinical trial process, as it establishes the starting point for pharmacokinetic and pharmacodynamic evaluations.
This process involves carefully selecting the appropriate dosage, formulation, and administration route to ensure optimal safety and efficacy.
Optimizing the First Fill process can enhance reproducibility, research accuracy, and experimental efficiency, ultimately leading to more successful drug candidates.
PubCompare.ai's AI-driven platform can assist researchers in locating the best protocols from literature, pre-prints, and patents, enabling informed decision-making and improved confidence in experimental design.

Most cited protocols related to «First Fill»

1
Assessing Antihypertensive Medication Adherence
The managed care organization’s data warehouse system was the source of the pharmacy fill data for the current study. As an Oracle relational database, the data warehouse was populated with historic claims data, patient roster data, diagnosis and procedural codes and code descriptions. Data were extracted by informatics analysts using the Oracle Discoverer tool, and transported into SAS 9.1. The pharmacy data were abstracted on 87 patients and included 42 different antihypertensive medications with 1578 fills captured in the study period.
Pharmacy fill data were extracted for the 2002 calendar year and included a listing of all antihypertensive prescriptions filled, the date filled, generic and brand names of the drugs, and number of pills dispensed. Three measures of adherence were calculated: continuous single-interval medication availability (CSA), medication possession ratio (MPR), and continuous multiple-interval medication gaps (CMG) [17 (link),18 (link)]. CSA was calculated by dividing the days’ supply obtained at a pharmacy fill by the number of days before the next pharmacy fill for that same medication. MPR was calculated as the sum of the days’ supply obtained between the first pharmacy fill and the last fill (supply obtained in the last fill was excluded) divided by the total number of days in this time period. CMG was calculated by dividing the total number of days without medications (i.e. treatment gaps) between the first and last pharmacy fill by the number of days in this time period. A graphical example of how CSA, MPR, CMG were calculated is provided in the Appendix.
For every participant, CSA was calculated for each pharmacy fill interval and MPR and CMG scores were calculated by class of antihypertensive medication being taken CSA and MPR values greater than one were truncated at the maximum value of one [19 (link)]. Given self-reported adherence reflects adherence to participants’ antihypertensive medication regimen, one CSA was assigned to each participant based on the mean of all CSAs calculated from all of their antihypertensive pharmacy fill intervals. One MPR and one CMG were assigned to each participant. For participants filling more than one class of antihypertensive medication, MPR and CMG were calculated for each class and then averaged across all classes to assign a single MPR and CMG to each participant. Given that a cut point of 0.8 has been previously used to define adequate medication adherence using pharmacy data [19 (link)-22 (link)] pharmacy fill nonpersistency was defined as <0.8 for CSA and MPR and >0.2 for CMG. Although other studies have reported continuous single interval gaps (CSG), this statistic is the inverse of CSA and, therefore, is not presented.
Krousel-Wood M., Islam T., Webber L.S., Re R., Morisky D.E, & Muntner P. (2009). New medication adherence scale versus pharmacy fill rates in hypertensive seniors. The American journal of managed care, 15(1), 59-66.
Publication 2008
Antihypertensive Agents Contraceptives, Oral Diagnosis First Fill Generic Drugs Managed Care Patients Pharmaceutical Preparations Prescriptions Treatment Protocols
2
Incident User Cohort Identification
The index fill was the first fill date for a qualifying medication in one of the three study cohorts in calendar year 2010. Individuals could be included in multiple disease-state-centric cohorts and the index date was allowed to vary between cohorts for such individuals. For each of the three cohorts, a series of five washout periods of increasing length – 3, 6, 12, 24 and 36 months – was used to assess prevalent use and create increasingly restrictive incident user cohorts (Figure 1). A treatment-naive definition of incident use was used, in which having at least one prescription claim for any cohort-specific study medication in the washout period, regardless of whether or not it matched the index drug or index drug class, constituted prevalent use. For example, an antihyperlipidemic user whose index medication was simvastatin would be considered a prevalent user if, in the given washout period, they had a claim for simvastatin, another statin or any other antihyperlipidemic agent. This is the strictest definition of prevalent use and is commonly employed in comparative effectiveness studies of pharmacotherapies, in which subjects are required to have never been treated for the condition under study to ensure similar baseline risks of the study outcome across groups. Inclusion in each of the incident user cohorts also required continuous insurance enrollment for the duration of the given washout period, defined by having a gap in coverage no greater than 7 days. This is necessary in incident user designs to ensure complete assessment of a subject's medication use for the entire washout period. A sensitivity analysis allowing a 30-day gap in insurance coverage was conducted; the results were unchanged, so the findings are presented using a 7-day coverage gap.
Roberts A.W., Dusetzina S.B, & Farley J.F. (2015). Revisiting the washout period in the incident user study design: why 6–12 months may not be sufficient. Journal of comparative effectiveness research, 4(1), 27-35.
Publication 2015
First Fill Hydroxymethylglutaryl-CoA Reductase Inhibitors Hypersensitivity Hypolipidemic Agents Pharmaceutical Preparations Pharmacotherapy Simvastatin
3
Predicting Opioid Overdose Risk in Medicare Beneficiaries
We compiled 268 predictor candidates, informed by the literature (eTable 4 in the Supplement).8 (link),9 (link),10 (link),11 (link),12 (link),13 (link),14 (link),15 (link),16 (link),17 (link),18 (link),19 (link),20 (link),21 (link),22 (link),23 (link),24 (link),25 (link),26 (link),27 (link),28 (link),29 (link),30 (link),31 (link) Patient, practitioner, and regional factors were measured at baseline in the 3 months before the first opioid prescription fill and in 3-month windows after initiating prescription opioids. We chose a 3-month window in accordance with the literature and to be consistent with the quarterly evaluation period commonly used by prescription drug monitoring programs and health plans.13 (link),14 (link),45 (link) In the primary analysis, we used the variables measured in each 3-month period (eg, the first) to predict overdose risk in each subsequent 3-month period (eg, the second) (eFigure 2A in the Supplement). In sensitivity analyses, instead of using a previous 3-month period to predict overdose in the next period, we included information collected in all of the historical 3-month windows to predict opioid risk for each 3-month period for each person (eFigure 2B in the Supplement).
The predictor candidates also included a series of variables related to prescription opioid and relevant medication use: (1) total and mean daily morphine milligram equivalent (MME),17 (link) (2) cumulative and continuous duration of opioid use (ie, no gap >32 days between fills),45 (link) (3) total number of opioid prescriptions overall and by active ingredient, (4) type of opioid based on the US Drug Enforcement Administration’s Controlled Substance Schedule (I-IV) and duration of action, (5) number of opioid prescribers, (6) number of pharmacies providing opioid prescriptions,11 (link),17 (link),23 (link) (7) number of early opioid prescription refills (refilling opioid prescriptions >3 days before the previous prescription runs out),46 (link) (8) cumulative days of early opioid prescription refills, (9) cumulative days of concurrent benzodiazepines and/or muscle relaxant use, (10) number and duration of other relevant prescriptions (eg, gabapentinoids), and (11) receipt of methadone hydrochloride or buprenorphine hydrochloride for opioid use disorder.19 (link),47 (link),48 (link),49 (link),50 (link)Patient sociodemographic characteristics included age, sex, race/ethnicity, disability as the reason for Medicare eligibility, receipt of low-income subsidy, and urbanicity of county of residence. Health status factors (eg, number of emergency department visits) were derived from the literature and are listed in eTable 4 in the Supplement.13 (link),16 (link),51 (link),52 (link),53 (link),54 (link) Practitioner factors included opioid prescriber’s sex, specialty, mean monthly opioid prescribing volume and MME, and mean monthly number of patients receiving opioids. Many beneficiaries had more than 1 opioid prescriber, in which case the practitioner prescribing the highest number of opioids was designated as the primary prescriber. Regional factors (eg, percentage of households below the federal poverty level) included variables obtained from publicly available resources, including the Area Health Resources Files, Area Deprivation Index data sets,55 and County Health Rankings data.56
Lo-Ciganic W.H., Huang J.L., Zhang H.H., Weiss J.C., Wu Y., Kwoh C.K., Donohue J.M., Cochran G., Gordon A.J., Malone D.C., Kuza C.C, & Gellad W.F. (2019). Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions. JAMA Network Open, 2(3), e190968.
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Publication 2019
Benzodiazepines Controlled Substance Dietary Supplements Disabled Persons Drug Overdose Eligibility Determination Ethnicity First Fill Health Planning Households Hydrochloride, Buprenorphine Hydrochloride, Methadone Hypersensitivity Morphine Muscle Tissue Opioids Opioid Use Disorder Patients Pharmaceutical Preparations Prescription Drug Monitoring Programs Prescriptions
4
Evaluating Medication Adherence Metrics
We constructed a hypothetical refill scenario commonly seen in reimbursement records for medicines for long-term conditions in order to illustrate the impact of variable elements on the calculations of adherence to single-medication such as hypertension. We selected seven dispensations in analogy to Steiner et al. [14 (link)] and calculated several indices. Two different observation periods of 250 days each were used: (1) from the first fill to the last refill date; and (2) over two arbitrary dates. Four calculations were performed:

The proportion of supply between dispensations or adherence in one refill interval (An/Bn), calculated as the days’ supply obtained at the beginning of a specific interval divided by the days elapsed before the subsequent fill and expressed as a percentage.

The days without supply between dispensations or gaps in one refill interval (Gn = Bn − An), calculated as the days elapsed before the subsequent fill i.e., the number of days between dispensations, minus the days’ supply obtained at the beginning of the interval.

The proportion of time with adequate supply or medication possession ratio (∑An/∑Bn), calculated as the total days’ supply obtained over the observation period and across all time intervals divided by the number of days of the observation period and expressed as a percentage.

The proportion of time without adequate supply or gaps over all refill intervalls (∑Gn/∑Bn), calculated as the total days of gaps (+) or surplus (−) divided by the total days to next dispensation or to end of observation period; that is, the cumulative sum of the number of days between dispensations minus the total days’ supply divided by the number of days in the observation period.

Arnet I., Abraham I., Messerli M, & Hersberger K.E. (2013). A method for calculating adherence to polypharmacy from dispensing data records. International Journal of Clinical Pharmacy, 36(1), 192-201.
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Publication 2013
First Fill High Blood Pressures Pharmaceutical Preparations Training Programs Vision
5
Bladder Strain Softening Quantification
A UD protocol was designed to directly translate our preclinical methodology established to quantify adjustable preload tension in isolated DSM strips4 (link)–11 (link) to human UD studies. To accomplish this, an extended repeat fill protocol with passive emptying and active voiding for specific fills was developed (Fig. 1A). An initial UD study was performed per best practice guidelines for clinical purposes and to determine maximum cystometric capacity (CCap).13 (link) For the initial fill, the fill rate was set at 10% of patient-reported maximum voided volume per minute on a 3-day void diary completed prior to the study. When the patient acknowledged that they had reached their maximum capacity (defined as inability to tolerate further filling or the presence of an involuntary bladder contraction), filling was stopped and the patient voided. Any post void residual was removed through the catheter via syringe aspiration, and the bladder was confirmed to be empty by an ultrasound technologist with transverse and sagittal midline suprapubic images taken at the onset and conclusion of each fill-and-empty cycle with a Philips Epiq 7 system with a 1–5 MHz abdominal probe (Amsterdam, The Netherlands). For all subsequent fills, CCap was defined as the sum of the voided volume and post void residual from the initial fill. Multichannel digital pressure and flow data acquisition was performed at 10 Hz via an Aquarius TT™ system (Laborie, Toronto). Four repeat fills were then initiated at a rate 10% CCap/min as follows: (i) fill to 30% CCap and passively empty; (ii) fill to 60% CCap and passively empty; (iii) fill to CCap and void (voluntary or involuntary); and (iv) fill to 60% CCap and void (Fig. 1A). Fills 1 and 2 were set to 30% and 60% CCap, respectively, to avoid triggering an active contraction which would limit the ability to identify the process of strain softening. Also, previous studies have used 33% stretches to effectively strain softening DSM strips.9 (link)Passive emptying at the end of Fills 1 and 2 was performed via syringe aspiration through a three-way stopcock in series with the infusion catheter so as not to alter calibrated pressures. The bladder was confirmed to be empty by ultrasound as previously described. Repeat Fills 1–2 were performed to progressively strain soften the bladder with incrementally increasing volumes (Fig. 1A). In a fluid-filled thin-walled vessel, pressure is directly related to wall tension by the Law of Laplace (tension ∝ pressure × radius); therefore, strain softening reflected by decreased wall tension should be identified by comparing the change in luminal pressure between fills. For the present protocol, the degree of strain softening was determined by comparing average intravesical pressure (pves) from 0% to 30% CCap in subsequent passive fills. The expected result was a progressive decrease in pressure (pves1 > pves2 > pves3) consistent with strain softening (Fig. 1B). The active void after Fill 3 was expected to reestablish the lost tension and demonstrate the reversibility of strain softening because preclinical studies show that active contraction reverses strain softening in DSM strips.4 (link)–11 (link) Thus, Fill 4 was expected to demonstrate pves similar to that observed in Fill 1.
Colhoun A.F., Klausner A.P., Nagle A.S., Carroll A.W., Barbee R.W., Ratz P.H, & Speich J.E. (2016). A Pilot Study to Measure Dynamic Elasticity of the Bladder During Urodynamics. Neurourology and urodynamics, 36(4), 1086-1090.
Publication 2016
Abdomen Blood Vessel Catheters Fingers First Fill Homo sapiens Patients Phenobarbital Pressure Radius Strains Syringes Ultrasonography Urinary Bladder Urination

Most recents protocols related to «First Fill»

1
Defining First-Line and Subsequent Therapies
First-line therapy was defined as the therapeutic product or procedure first observed for the patient within 30 days of the index date, and for pharmaceutical agents, if repeated within 60 days post-index. First-line combination therapy was assigned if a claim for a pharmaceutical agent occurred within 30 days following an index phlebotomy treatment, or if a phlebotomy procedure or claim for a second drug was reported during the active period of a prescription (defined as remaining number of days-supply plus a 30-day grace period) for a drug prescribed at index.
Second and third therapy lines were identified at the next change in therapy by addition, switch, discontinuation, or restart. A new treatment was considered an addition if its first prescription fill or treatment date occurred within the active period of a prior therapy, and the prior therapy was repeated within the active period of the new treatment, irrespective of duration. A new treatment was considered a switch if the prior therapy was not repeated within the active period of the new treatment. Discontinuation was identified if a therapy was not repeated within its active period and the active period did not include the end of available data. Restart was identified if a discontinued therapy recurred in claims (Online Resource 1).
Verstovsek S., Pemmaraju N., Reaven N.L., Funk S.E., Woody T., Valone F, & Gupta S. (2023). Real-world treatments and thrombotic events in polycythemia vera patients in the USA. Annals of Hematology, 102(3), 571-581.
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Publication 2023
Combined Modality Therapy First Fill Patients Pharmaceutical Preparations Phlebotomy Therapeutics
2
Slope Length and Steepness Factors Calculation
The next step in the methodology was the S- and L-factors calculation. The latter determines the impact of slope length due to the distance between the point of origin of overland flow and the deposition point (or channel). Furthermore, the S-factor accounts for the effect of slope steepness, being higher when the slope is steeper [73 ,74 ]. Following the approach of Desmet and Govers [75 ], the L-factor can be derived through:
Li,j=(Ai,j−in+D2)m+1−Ai,j−inm+1Dm+2*xi,jm*22.13m
m=β(1−β)
β=(sinΘ0.0896)[0.56+3*(senΘ)0.8]
where Li,j is the impact of slope length factor for cell (i,j); Ai,j−in is the contributing area at the inlet of cell (i,j) measured in m2; D is the cell size in m; Xi,j = sinαi,j+cosαi,j; αi,j is the aspect direction of cell (i,j); m is the ratio β of the rill to interill erosion; and Θ is the slope angle in degrees. To complete the S-factor, the following equations are applied:
S=10.8*sinΘ+0.03,Θ<0.09
S=16.8*sinΘ−0.5,Θ≥0.09
where S is the S-factor. To derive them, we used the System for Automated Geostatistical Analysis [SAGA-GIS; 76 ] to first fill surface depressions of the SRTM-DEM (See Section 3.1) with the function “Preprocessing—Fill sinks (Wang & Lui)” [77 ] to force areas to flow downstream where pooling occurs. Then, we applied the function “Hydrology—LS factor, Field Based” [78 ], which outputs the multiplication of S- and L-factors. Despite this factor is dimensionless, we obtained an average of 11 (SD = 21) for all of the study area, where it averaged 7.6 (SD = 10.9) on the higher slopes (>25°), and 12.4 (SD = 22.4) on the lower slopes (<25°).
Santos F., Calle N., Bonilla S., Sarmiento F, & Herrnegger M. (2023). Impacts of soil erosion and climate change on the built heritage of the Pambamarca Fortress Complex in northern Ecuador. PLOS ONE, 18(2), e0281869.
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Publication 2023
First Fill Gene Flow STEEP1 protein, human
3
Medicare Hypertension and Cognitive Outcomes
We conducted a retrospective cohort study of Medicare beneficiaries with hypertension. We used Medicare claims data from January 1, 2006, through December 31, 2018, for a 5% random sample of Medicare beneficiaries from the Centers for Medicare & Medicaid Services. Figure 1 shows the study design, outlining the timing of measurement of antihypertensive medication use and study outcomes. Beneficiaries were included in the study if they had a new diagnosis of hypertension from January 1, 2007, to December 31, 2014; the first occurrence of a diagnosis was defined as the index date. This time frame allowed for at least 4 years of follow-up for all study participants (because data were available through 2018). To ensure beneficiaries had a new diagnosis of hypertension, we applied a 365-day baseline washout period prior to the index date. Beneficiaries with a diagnosis of hypertension or a claim for an antihypertensive medication during this 365-day washout period were excluded. We ascertained use of antihypertensive medication starting on the index date. The day of the first fill for an antihypertensive medication that stimulates or inhibits type 2 and 4 angiotensin II receptors (list of medications in eTable 1 in Supplement 1) after the index date was defined as the treatment initiation date. We measured outcome events (ADRD and vascular dementia) starting 360 days after the treatment initiation date. We applied this blanking period because ADRD and vascular dementia develop over time; therefore, we assumed a minimum exposure was needed for the antihypertensive medication to plausibly have an appreciable association with cognitive outcomes. Outcome events were collected from the end of the blanking period until the occurrence of an outcome event, death, disenrollment, or end of the study period. This study was deemed exempt by the institutional review board at the University of California, San Diego, because it used deidentified claims data. Informed consent was not obtained due to the exempt status of this study. This report follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies.
Marcum Z.A., Gabriel N., Bress A.P, & Hernandez I. (2023). Association of New Use of Antihypertensives That Stimulate vs Inhibit Type 2 and 4 Angiotensin II Receptors With Dementia Among Medicare Beneficiaries. JAMA Network Open, 6(1), e2249370.
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Publication 2023
Antihypertensive Agents Cardiac Arrest Cognition Dementia, Vascular Diagnosis Dietary Supplements Ethics Committees, Research First Fill High Blood Pressures Pharmaceutical Preparations Reading Frames Receptor, Angiotensin II
4
Postpartum Exercise Intervention Protocol
For the promotion and implementation of the exercise program, the following procedures were followed.
Before the intervention:

Preparation and promotion of the educational materials [42 ,43 ,44 ,45 ,46 ].

Training qualified exercise professionals [47 (link)].

Preparation of the physical practice space: authorization request to the person responsible for the “Lateral Performance” training space to carry out the program, as well as the use of the respective equipment.

Promotion of the exercise program on social networks (Facebook and Instagram).

Recruitment of participants by completing an online form.

Explanation to the participants about the objective and pertinence of the study and the importance of their collaboration and availability in this research.

Informed consent: statement informing the study objectives, information about the assessment sessions, confidentiality, participation and abandonment, damages related to the investigation, exclusion criteria, and disclaimer.

Medical clearance for the postpartum exercise program.

Initial assessments at baseline:

Initial individual interview to fill out the participant form to obtain sociodemographic and clinical information, as well as potential barriers and preferences regarding the intervention.

Measurement of parameters of physical activity, quality of life, health, physical fitness, and functionality (between 6 and 8 weeks postpartum).

Measurement of blood pressure and resting heart rate, as well as calculation of reserve and maximum heart rate.

Assessment of BMI and body composition.

Measurement of waist and hip perimeters, calculation of the waist-hip ratio.

Postural assessment, with static observation of the anatomical references in the frontal and sagittal planes, verifying the symmetry in relation to the imaginary midline and photographing the same.

Application of some tests from the Battery of Functional Aptitude Tests Dynamic Neuromuscular Stabilization (DNS), namely the seated diaphragm test, intra-abdominal pressure test, quadruped rockforward test, and qualitative recording of the results.

Application of some tests from the Battery of Functional Aptitude Tests Functional Movement Screen™ (FMS™), such as shoulder mobility, active straight leg raise, deep squat, rotary stability, and qualitative recording of results and scores.

Assessment of cardiorespiratory fitness using the Rockport One-Mile Fitness Walking Test and recording time, heart rate, and calculation of maximum oxygen consumption (VO2max).

Assessment of muscular endurance, counting the maximum number of arm extensions (push-ups) and applying the “Chair Stand Test,” registering the maximum number of repetitions in 30 s.

Assessment of flexibility, with the “V-sit and reach test.”

Intermediate (after 8 weeks) and final assessment (after another 8 weeks):

Assessment of parameters of physical activity, quality of life, health, physical fitness, and functionality.

After the final assessment:

A form was sent to participants to collect feedback on the level of satisfaction with the exercise program, containing the following questions inspired by Haakstad et al. [48 (link)]:

Level of satisfaction with the program.

Level of satisfaction with the instructor.

Do you consider that exercise in a group environment was/would be more motivating than if it were individual?

In what parameter (s) did you feel improvements in terms of your physical fitness?

Have you changed your physical activity levels?

Do you feel more energy for daily activities and less stress?

Would you recommend this program to a friend?

Would you participate in the program again after another pregnancy?

Would you like to leave other comments?

Brites-Lagos C., Ramos L., Szumilewicz A, & Santos-Rocha R. (2023). Feasibility of a Supervised Postpartum Exercise Program and Effects on Maternal Health and Fitness Parameters—Pilot Study. Healthcare, 11(20), 2801.
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Publication 2023
Abdominal Cavity Aptitude Tests Body Composition Cardiorespiratory Fitness Determination, Blood Pressure Feelings First Fill Friend Mental Tests Movement Muscle Tissue Oxygen Consumption Perimetry Physical Examination Postpartum Programs Pregnancy Pressure Range of Motion, Articular Rate, Heart Satisfaction Shoulder Surgical Clearance Waist-Hip Ratio Walk Test
5
Measuring Medication Adherence Using PDC
The primary outcome was change in adherence in the presumed parent of each eligible child. Adherence was assessed by applying the proportion of days covered (PDC) metric to pharmacy claims data, a standard quality measure.27 (link),28 (link),29 (link) To measure PDC, we generated a supply diary for the parent’s medication by aggregating consecutive fills based on dispensing date and days supplied, starting 539 days before the child’s index date (or the parent’s first fill before that date) through 360 days after the index date.11 (link) We allowed supply to accumulate to 180 days; days spent in a facility were considered covered days. Parents were censored on loss of enrollment.
Using this supply diary, we calculated PDC within each month (normalized to 30 days) from 360 days before the index date to 360 days after (24 months total). Overall, PDC was calculated by dividing the number of days with medication available each month by the number of days the parent contributed that month. We measured PDC as both a continuous (primary outcome) and secondary dichotomous adherence measure, with PDC of 80% or greater considered optimal.27 (link),30 (link),31 (link)
Lauffenburger J.C., Barlev R.A., Olatunji E., Brill G, & Choudhry N.K. (2023). Costs of Prescription Drugs for Children and Parental Adherence to Long-Term Medications. JAMA Network Open, 6(10), e2337971.
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Publication 2023
Child First Fill Parent Pharmaceutical Preparations

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1
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More about "First Fill"

The First Fill process is a critical step in drug development and clinical trials, as it establishes the initial dosage, formulation, and administration route for pharmacokinetic and pharmacodynamic evaluations.
Optimizing this process can enhance reproducibility, research accuracy, and experimental efficiency, leading to more successful drug candidates.
PubCompare.ai's AI-driven platform can assist researchers in locating the best protocols from literature, pre-prints, and patents, enabling informed decision-making and improved confidence in experimental design.
The First Fill refers to the initial dose or application of a medication or treatment.
This process involves carefully selecting the appropriate starting point to ensure optimal safety and efficacy.
Synonyms for First Fill include initial dose, initial application, and starting point.
Related terms include pharmacokinetics, pharmacodynamics, drug development, clinical trials, and experimental design.
Abbreviations commonly used in this context include PK (pharmacokinetics), PD (pharmacodynamics), and RCT (randomized controlled trial).
Key subtopics include dosage selection, formulation optimization, administration route, reproducibility, and research accuracy.
To enhance the First Fill process, researchers may utilize various software and equipment, such as SAS version 9.4, Polar OH1, Model 8453, Prismaflex, Stata version 14, SPSS software, SAS for Windows version 9.4, Pump 11 Elite Programmable Syringe Pump, and R software version 4.0.3.
Additionally, Fluorescite may be used as a fluorescent dye to visualize and analyze drug distribution.
By incorporating these insights and tools, researchers can optimize the First Fill process, leading to more successful drug development and improved experimental outcomes.