Pharmacokinetics - Science topic

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yes i worked two times but this model not properly works in excel.This model given my senior.After i used other software.

Dear Murugesan Arumugam,

Pl refer link (page 22-23) for calculation of loading and maintenance dose based on PK parameters.

Regards

Hitesh Chavda

David Salehi Thank you for your recommendations. I will certainly have a look at them.

based on a double-blind, multicenter, RCT comparing 1,000 mg tabs vs 500 mg tabs (same daily dose) in acute hemorrhoid, the authors concluded that "We have been demonstrated that the new single MPFF 1000 mg tablet has clinical acceptability and a good safety profile, comparable to that of MPFF 500 mg tablets. MPFF 1000 mg was as effective as MPFF 500 mg in reducing anal pain and bleeding. The new dose regimen should lead to better treatment adherence and consequently to better management of hemorrhoidal disease."

Hi,

Sounds like a basic question but is very often asked by experienced scientists. The answer is it depends on your goal. Measurement PK does not always mean analysis 'only' PK so the dose will be different if:

And many others factors and goals may change decisions about dose amount. But one critical is a range of validation of your analytical method. If for example, LLOQ of your method is 10 ng/mL you can't use a dose that gives concentrations between 0.01-3.0 ng/mL. Consequently in your PK study:

Please remember that the calculation is very very general because depending on study item many processes may change this value within seconds (blood protein binding, first-pass effect, route of administration, and presence of absorption phase etc). So if possible always if you have no data which can be translated from other animals or homologs try to make a pilot study it is always very helpful for both optimizations of the analytical method and dose level. The pilot could be done only with a few animals and a few samples of blood.

Some useful tips related to single-dose PK you can find in my paper below:

Best regards

Tomasz

Hi, how about this site?

NCA: http://nca.kscpt.org/

Compartment modeling: http://ca.kscpt.org/

I guess when concentration becomes too low, you might have to look for cues like calibration of the detector, and other variables such as temperature and pressure which affect rate of the reaction. Solubility and saturation might also be considerations. A slow reaction might take a longer period of time to measure.

Hi,

The best way for sampling from small animals is the sparse sampling method (if you have no access to cannulated animals + a very sensitive method). In the case of sparse sampling, you can implement the options present in software for example Phoenix WinNonlin (see reference below). Even if you work with very small animals or sparse sampling rules related to PK analysis are in close link with study design and PK curve structure. Those rules are the same like in humans or large animals If its typical single dose study with small population (excluding for example large populations and population PK were sampling is different or therapeutic drug monitoring etc.). Consequently, for example, 7 sampling points per typical single dose, frequent sampling curve in an exploratory study is not folowing the rules. Descriptiion of expectations related to typical frequent sampling in single dose PK studies (not population methods of analysis) you can find in my paper which shows some compilation of current consensus both in science and regulatory trials.

These are the principles that follow the principles of analytical geometry & mathematics. It is the common denominator of these rules. If you follow these rules, you will exclude any BIAS resulting from the lack of an optimized sampling design. The last rule about Clast Cmax will prevent you from calculating an elimination rate constant based on distribution phase rather than elimination. For example, if your expected Cmax is 100 ng/mL your analytical method LLOQ should be around 1 ng/mL rather than 10-15 ng/mL. If you follow these rules, you will always be on the safe side. To implement them, the best solution is a pilot analysis with a few animals before starting the main study and a validated sensitive method of analysis.

Hope this comment could be useful,

Best regards

Tomasz

Sparse sampling reference: https://onlinehelp.certara.com/phoenix/8.3/topics/Sparse_sampling_calculation.htm

.

Dear Sherouk,

Another thought I had is that you may be able to study the correlation differently.

Is the biomarker expected to change very rapidly in response to drug? Is this why you want to correlate to Cmax? Is it also going to be itself eliminated very quickly when drug concentrations decrease?

If these conditions are met you could try to fit an Emax / Imax model to the data (biomarker as a function of drug concentration at any time).

For this to makes sense you would need the biomarker to have a fast turnaround (faster than drug kinetics) and to show fast response to drug.

Respected Prof Faraz Khurshid , I cannot agree with you more. I think PD/PK are really subjects that needs to be emphasized in modern pharmacy training. We really need fundamental and basic scientific training for every pharmacist. Scientific concept is important for clinicians.

I also like evidence-based medicine, I guess this is really an important direction for our future generations. It is the black and white evidence that really underlie our decision and we should record the evidence accordingly so that we are speaking the same language and sharing our knowledge.

It is important that every decision is recorded and traceable, so that we are recognized as a professional and/or expert in a particular area(s).

Hi Manikandan. Interesting exercise. Kindly see the attached workout. I hope it will guide your further efforts on the assignment.

In summary, it appears from the first table that:

1. Compounds 2 and 3 exhibit lower clearance, and higher bioavailability.

2. Compared to Compounds 1 and 5 (which exhibit equipotency), Compounds 2, 3, and 4 exhibit lower potency, Compound 2 much less so than the others.

3. Compound 4 exhibits lower bioavailability, a lower Cmax and, higher clearance.

4. Compound 5 exhibits the lowest Cmax but with unknown bioavailability and clearance. Safe to assume a high Vd and thus high clearance from the central compartment, and low bioavailability.

Dear Siam,

I'm afraid I haven't come across allometry for such cases. In general allometry is used to scale between mammalian species (or individual of different sizes for pediatric applications).

The general idea is that flows (clearance) scales with a factor of about 0.75, so this applies also to dose (which is expected to be a function of clearance). So it follows a relationship of a*BW^0.75.

In absolute terms you could apply the formula with the weight of any species / individual. However, I suspect that the empirical principles mainly established between mammals may not apply to invertebrates.

To note that even between more similar species like mammals allometry does not always work well.

Out of curiosity, how do you apply drugs in insects? Can you actually apply orally or how it it done?

The words selectivity, specificity, and sensitivity (derived from Latin seligere, specificus, sensitivus), can be confusing terms as they are often used synonymously in the medical literature. However, they should not be used

interchangeably as each represents a different phenomenon.

Reference:

Mencher, S. K., & Wang, L. G. (2005). Promiscuous drugs compared to selective drugs (promiscuity can be a virtue). BMC clinical pharmacology, 5(1), 1-7.

many thanks doctors for your response..

would you recommend any supporting articles for this area of study Falguni Manoj Saraswat Tohlang Sehloho

This paper could address the issue:

There are many iso enzymes that metabolism drug ,not necessarily the both Niacin and statin have same iso enzyme.

Important other important factor is the dose of Niacin may be very low to elicit metaboliic process.

@karel

I wish to know if there's any genetic polymorphism/variation in the pharmacology of frusemide. Whether at the level of safety, pk or pd?

Is there anything of concern in this regard sir?

Hi Ashish,

You can do NCA analysis of all derived data if you have a full profile in organs.

Additionally from tissue data, you can derive tissue to plasma ratio. It will help you in understanding the drug disposition in various tissue.

From the CT profile of the animal, you can extrapolate the various doses and dose regimens by compartmental analysis. if you have sufficient subject then you can derive information about covariate impact on PK by popPK.

if you need further assistance let me know.

That makes sense. Could you be referring to C0 instead of Cmax instead? C0 is the extrapolated value to time 0, which will be Dose/Vd. If C0 changed and your dosing is constant, it is likely that there was a difference in volume of distribution that occurred. I generally find Vd harder to explain biologically as the reason for the change could be anything from changed transporters in blood cells to fat or other peripheral tissues or plasma protein binding.

In IV data, Ke = CL/Vd. If CL changed, but Ke did not, it is possible that Vd also changed to similar extent. Ke = ln2/(t1/2), so if Ke does not change, t1/2 will not change. An easy way to see this would be (as a quick and dirty way of ensuring your calculations were correct), if you plot your plasma data on a log 10 y axis, do the gradients of each mouse group look similar? Ke is pretty much the slope on that graph. Here is a list of useful PK equations that I often reference to understand such plots better. https://pharmacy.ufl.edu/files/2013/01/5127-28-equations.pdf

It is also possible that the virus you were administering did not do what you expected. Perhaps other things to investigate post sac, e.g. kidney histology - did the virus damage the kidney tissue?

Thank You Riaz & Tejas

Hi,

Unfortunately, there are no such methods. Current solutions and models do not allow for selective analysis of one or several substances after administering the extract (.... which may contain several substances). Such analysis will only inform about interactions between constituents nothing else. Of course, the extent of these PK interactions will depend on the composition of the extract. What you can do is determine the concentration of the dominant substance in the extract and try to determine its concentration in blood samples or urine. However, the cognitive and scientific value of such an analysis will still be low. If an extract is administered, it is not possible to associate a single substance with a pharmacodynamic effect (PD). Currently, we do not have such data analysis capabilities. The effect of PD after administration of the extract is always the result of interactions between all components of the extract. We currently have neither software nor models that can separate these interactions from each other. Therefore, PK and PD are tested with the use of single selected substances or in systems where interactions with one substance are tested, but not with so many.

All you can do is try to characterize the chemical composition of the extract and describe/analyze the PD effect. Selective analysis of PK or selective PD analysis in your case is not possible.

Best regards

Tomasz

Hi

There could be many reasons why you do not see typical absorption phase after oral administration in your PK profile for example:

Best regards

Tomasz

Arthur Saus I agree with you

Interpolation can be thought of in two ways:

1) Interpolation methods approximate some underlying model

2) Interpolation simply approximates a set of numbers continuously

In case 1), interpolating will capture the essence of the underlying function, if and only if, the data is representative of the fundamental model. Noise will definitely throw your results off.

In case 2), you are simply generating an approximation from a set of known numbers, e.g. Lagrange interpolation; forward, centered and backward Newtonian interpolation, &c. The numbers in hand may or may not represent anything in particular, or, they may not be accurate enough to enable one to gleam the underlying processes. Purely interpolated results can and do grow wild as the power of interpolation is increased.

What I suggest is a low power interpolation of the database, then look at the curve and compare to any model you have in mind. If the interpolated results resemble strongly enough some model, then you may further pursue investigating along that vein.

Hi

"enantioselective pharmacokinetics" please check the keyword in google or NCBI.

geometrical isomers have different physicochemical characteristics for example pKa, the same problem cover diastereoisomers.

D-dopa is less absorbed than L-dopa

D-methotrexate is less absorbed than L-methotrexate

cis - lycopene is absorbed to a greater extent than trans - lycopene

L - cephalexin inhibits the absorption of D - cephalexin from the gut

R-methadone has a greater volume of distribution than S-methadone

R - (-) - disopyramide binds less to blood proteins than S - (+) - disopyramide

Cis-cis - mivacurium has a larger volume of distribution than cis-trans, trans-trans

S - propranolol binds more strongly (competes) with plasma proteins than R - propranolol

R - sulbenicillin binds to plasma proteins weaker than S - sulbenicillin

R - latamoxsef binds to plasma proteins more strongly than S - latamoxsef

R-carbenicillin binds to plasma proteins more strongly than S-carbenicillin

R - propafenone delays the metabolism of S - propafenone

S - nitrendipine is an inhibitor of R - nitrendipine metabolism

R - verapamil is less affected by the first-transition effect than S - verapamil

R - ketoprofen in most species is transformed into S ketoprofen (rodent, dog, monkey, horse, cat) the Asian elephant is the only species that converts S into R

A very interesting case is a chiral inversion in the liver (see examples below)

flobufen (Skalova L. et al 2001)

ibuprofen (Doki K. et al 2003)

pranoprofen (Imai T. et al. 2003)

ketoprofen (Lees P. et al. 2003)

fenoprofen (San Martin M.F. et al. 2002)

albendazole (Virkel G. et al 2002)

thalidomide (Erikson T. et al. 2001)

clopidogrel (Reist M. et al. 2000)

D-leucine (Hasegava H. et al. 2000)

thiaprofenic acid (Erb K. et al 1999)

pantoprazole (Masubuchi N. et al. 1998)

styripentol (Tang C. et al. 1994)

lifibrol (Walters R.R. et al. 1994)

Tolperizon (Yokoyama T. et al. 1992)

R-methadone has a longer half-life than S-methadone

R-ibuprofen has a shorter half-life than S-ibuprofen

(-) - mefloquine has a longer half-life than (+) - mefloquine

R - (-) - ketamine inhibits the elimination of S - (+) - ketamine

(+) - terbutaline inhibits tubular reabsorption of (-) - terbutaline

R - sotalolol reduces the renal clearance of S - sotalolol

R - flurbiprofen is excreted from the bile to a greater extent than S - flurbiprofen

R - carprofen is secreted from the bile to a greater extent than S - carprofen

R - sulbenicillin has a lower renal clearance than S - sulbenicillin

R - (+) - propranolol is eliminated more slowly than S - (+) - propranolol

Z - doxepin has a stronger antidepressant effect than E - doxepin

Z - doxepin has a stronger antidepressant effect than E - doxepin

S (-) bupivacaine is less toxic than racemic bupivacaine

Of course, it's only the peak of the iceberg so, please check:

.... now > 1000 results ....

examples with references are available in my free e-book (polish version only)

Best regards

Tomasz

In addition to the answer of Geertje you should carefully purge the vascular system by perfusion of saline or an appropriate buffer

best regards , Gert

You can use the device for PCcaps Capsules, which is designed for rats.

it depends on your hypothesis, how big do you anticipate a difference will be ? this difference should at best be of clinical relevance, so that if the study is negative, that we have learned that this covariate is not of clinical relevance ?

Many thanks @ Simon AA Davis

Hello,

The best software isn't free. It's expensive. You can try the PKMP trial version https://aplanalyst.com.

Hello Anna,

Lots of thanks for the response and the link was very helpful.

I am grateful indeed.

Hi

Conclusion about no interactions probably cover lack of meaningful PD observations see:

" Candesartan cilexetil produced a 7% decrease in trough plasma warfarin concentration but this had no effect on prothrombin time. "

So in some cases pharmacokinetic interaction can exist but have no impact on clinical findings. Probably this is why you find conclusions about lack of interaction. Finally protein binding in blood plasma not must be most important aspect of interactions between both drugs

from another hand:

" Candesartan had no effect on S-warfarin 7-hydroxylation. In contrast, S-warfarin inhibited candesartan metabolism by the wild-type (K = 17microM) greater than by the Leu359 variant (Ki = 36 microM). These findings suggest that CYP2C9*3 may change not only the metabolic activity but also the inhibitory susceptibility compared with CYP2C9*1. "

Best regards

Tomasz

Hi

To be onest from my point of view best are FDA documents related to specific drug submission its better than any database (for example drugbank https://go.drugbank.com/drugs/DB00341) ....

for example

google, key words: fda cetirizine pharmacokinetics accedata

Or maybe such books will be better

Best regards

Tomasz

Hi,

You can't predict or simulate S.C. pharmacokinetics without knowledge about bioavailability after S.C. administration in vivo. After you have this information you can simulate or calculate „treatment” – multiple-dose experiments. If you have no flip-flop effects or no issues with non-linear PK then sampling intervals after S.C. should be more less in the same range (zero-last sampling point) as in IV. So planing S.C. should not be very complicated. Of course, you should use some let say general rules for sampling optimization in singe dose studies. See the table 2 from my paper below. Should be useful.

Best regards

Tomasz

Please bear in mind that there is a large interindividual variability in the bioavailability and therefore the plasma concentration of parent mycophenolate and active metabolite. The variation can b as large as 30%. You may consider checking the plasma concentration in critical cases.

Hi

Please try sparse sampling method. For example for one time point you can take single sample from 1-3 different rats (rat number 1, 2, 3) for next point single sample from 3 next rats (rat number 4, 5, 6). You can reduce number of animals and leave them alive.

Examples how software can solve such set of data in NCA PK:

Example of study design:

I hope this helps you .... and your rats as well :-)

Best regards

Tomasz

Ages, race, diet, pathological state

Yes. I did.

To establish the calibration curve in a matrix similar to your unknown samples, in this case your matrix is plasma, is considered the best option to account for matrix interference with your analytes of interest. It is known as "Matrix-Matched Calibration". It is a common practice when you expect an intereference that may decrease or increase the analytes' signal in the Mass Spectrometer.

To establish the calibration curve in a matrix similar to your unknown samples, in this case your matrix is plasma, is considered the best option to account for matrix interference with your analytes of interest. It is known as "Matrix-Matched Calibration". It is a common practice when you expect an interference that may decrease or increase the analytes' signal in the Mass Spectrometer.

Thus, to answer your question in simple words, yes it is necessary, at least from analytical point of view.

You may refer to some publications about matrix effect in mass spectrometry, like on of my papers in my profile.

https://books.google.az/books?id=uyQXqQH4NAMC&pg=PA3509&lpg=PA3509&dq=Is+it+scientifically+appropriate+to+dose-normalise+non-linear+compound+kinetics?&source=bl&ots=YTL-kUHvtf&sig=ACfU3U1_fCwMN0vTK-XFBkfEmSU07-nTCw&hl=ru&sa=X&ved=2ahUKEwiUm8ychKPqAhVLhqQKHd1-BVgQ6AEwAXoECAoQAQ#v=onepage&q=Is%20it%20scientifically%20appropriate%20to%20dose-normalise%20non-linear%20compound%20kinetics%3F&f=false

Dear Goutam Kumar Jena

As previously mentioned by Siddhartha Dutta

I proposed that the http://www.swissadme.ch/ is the best choice

All the best

Por su puesto esto depende de la Biodisponibilidad de cada sustancia y las condiciones de su administración y eliminación. Lo más importante es como afecta la Seguridad y la Eficacia en Terapéutica

Michaelis–Menten kinetics is one of the best known models of enzyme kinetics in in vitro drug elimination or drug-drug interaction experiments. it consists of two parameters, the maximum reaction rate (Vmax) and the Michaelis constant (Km) describing the rate of enzymatic reactions by relating reaction rate (V) to the concentration of a substrate ([S]). By definition, Km is equal to the concentration of the substrate at half Vmax. It can also be thought of as a measure of how well a substrate complexes with a given enzyme, otherwise known as its binding affinity. An equation with a low Km value indicates a large binding affinity, as the reaction will approach Vmax more rapidly.

Go forAlamar Blue assay that does not require lysis at all.

The publications of Peter Meier describing isolation of hepatic membrane vesicles may be helpful. e.g. J Cell Biol. 1984 Mar;98(3):991-1000.

Hi Andy,

Link works correctly but if not in your PC then try with phrase "boomer pk course" in google.com and click first link from the top,

Best regards

Tomasz

Dear Tejosmita Sen

Few points should be useful I hope:

- develop bioanalytical method for your molecule determination in blood plasma (or blood plasma and whole blood after hemolysis) without a method you cant do your PK study. Moreover you cant make next steps.

- check ex vivo using blank full blood cell fraction and blood plasma, or hemolysed full blood (frozen blood) does your molecule transfer to RBC or not (then you will know what analytical matrix will be useful for you.)

- Calculate LogP of your molecule. If its lipophilic structure with LogP more less >1 then please remember how big impact it could have for binding with plasma proteins (recovery of your analytical method), affinity for other proteins for example with enzyme activity (CYP) (first pass effect possible).

- decide about route of administration and local toxicity (local toxicity should be a part other tox studies) If IV is possible its easier route for you, if not IP could be usefull. Please remember after IV take first samples immediately after drug administration for example 30 sec, after drug administration 2 min 5 min 15 min 30 min ...... its most important samples because show close to t0 observed concentrations which can go dawn with very high rate.

- use tox studies for planning dose which could be well tolerated

- check carefully what is available in relation to monologues (NCBI etc.)

- make pilot study using 3 animals and for example three doses. Example 1mg/kg (one animal), 3 mg/kg (one animal), 9 mg/kg (one animal). If its IV from fist take sample after 30 sec, from next after 30 sec and from last after 3 hours. Analyse data and trends for first PK study sampling optimization.

- With first PK mice study my suggestion is applying sparse sampling. and two stage approach if sampling will be to short to cover whole PK.

Best regards

Tomasz

Sperm is typically prevented from exposure to blood - because even a male's own sperm is seen by their immune system as "foreign", leading to the formation of anti-sperm antibodies. This can be a problem in clinical situations such as after testicular trauma, leading to infertility.

As such, I dont think sperm delivery into the bloodstream is practical and although I dont have any specific data on it, I believe the survival of sperm in the blood stream would not be very long. The article you have linked to seems to be suggesting the utility of a sperm delivery system for anti-tumour therapy into the female genital tract (in the lumen, not via the bloodstream) to treat malignancies like endometrial cancer. I couldnt access the full text, so not sure how rigiriusly they have tested this - certainly sounds like an interesting idea!

It is better to use AIC than R squared for curve fitting in that case you have two models with high correlation R2 more than 0.99

Dear Georgi,

Looking in vitro studies looks that chitosan persist long time and could pass from mother to daughter cells through several mitotic cycles.

" Many polymers (especially the chitosan) and their nano/microparticles, commonly referred to as biodegradable/biocompatible, have been investigated for drug delivery and other medical purposes; however, the mechanism of their degradation has not been fully elucidated in biological systems. Evidence for their in vitro or in vivo degradation is very limited or conflicting. The FDA has approved some polymers for drug delivery and certain medical applications [5] because their degradation products are deemed to be biocompatible and eliminated from the body. However, once these polymers have formed nanoparticles by crosslinking (either by covalent, ionic or other bonds), it is not known whether those nano/microparticles are entirely biodegradable or biocompatible. "

source:

It looks like we currently don't have enough data to say that chitosan doesn't have deep compartments. If it is transmitted from cell to cell in mitotic cycles, this indicates strong binding to cell structures and a very low level of elimination. It is very likely that some fraction of chitosan may be eliminated very slowly and stay in the body for a long time. Chitosan with more 73% degree of deacetylation is hardly degraded. Highly deacetylated chitosan (over 85%) shows a low degradation index in the aqueous environment and will degrade after a few months. This means that the kinetics of elimination of this fraction is non-linear. It also requires the creation of more complex pharmacokinetics models. Until now such models probably are not described in literature.

Best regards

Tomasz

I believe you need to change some of the solvents used, as as Elena V Romanova said, their RT may be the same or similar to your analyte.

Best of luck.

Dear Naïrouz Benzeggouta

If I correctly understand your question is not about food impact on drug PK but exactly "pharmacokinetics" of food components. Its very interesting question. Of course term "pharmaco" could be used only for drugs not for other substances.

In general food contents take part exactly the same process like drug after per os administration :

Food intake is continuous process, that means substances available in the food are absorbed regularly after meal. In such situation we can talk or describe in scientific manner "kinetics" of some food components (single well defined substance) but not "food pharmacokinetics".

In case of food components is very complicated because many components of food are absorbed in very composite environment from chemical and physiological point of view. Food per se change pH, motility, blood perfusion, and other features of the stomach. That change cover not only stomach but bile ejection and other process influencing absorption every single substance - content of the food. Finally one single meal could deliver millions different substances into gastro-intestinal tract (GIT) which interact between each other in absorption phase and other kinetic (not pharmacokinetic) phases .....

Concluding your question there are many differences between

But in both cases we can explain mechanisms and processes using current tools proposed by pharmacometrics.

Hope it helps

Best regards

Tomasz

Hi Frederick Vinckenbosch , yes, you're right. By solving the equation you get 0.25084 mg/l (by using the original units: 20mg and 84L (from 1.4 L/kg*60kg)) which equals 0.25084 µg/ml or 250.84 ng/ml.

Problem solved, then!

Regards, Luis

I work for Certara who write and support Phoenix WinNonlin;

we also provide lots of training options to get the best from the software.

https://www.certarauniversity.com/learn/course/external/view/elearning/26/100-od-introduction-to-phoenix-winnonlin

Simon.

I have found this tool in the web, I think that it uses the IC of 90% and acceptance criteria of 80-125. Please check.

http://epitools.ausvet.com.au/content.php?page=bioequivalence

Hello Yanfei, it is highly probable that your extract inhibited the metabolizing enzymes for irinotecan. Co-administration of certain agents either induces the metabolizing enzymes (leading to rapid clearance/elimination, decreased duration of action and perhaps therapeutic failure) or inhibits the said enzymes, leading to slow clearance/elimination, increased plasma concentration, increased duration of action and perhaps toxicity.

Was this helpful?

You can go through these links:

https://www.sigmaaldrich.com/content/dam/sigma-aldrich/docs/Sigma/Product_Information_Sheet/2/m5391pis.pdf

Scale-up effects on dissolution and bioavailability of propranolol hydrochloride and metoprolol tartrate tablet formulations.

Thank you, Dr. Tomasz and Dr. Patrice.

Dear Hui Guo

If last sampling point (Clast) will be analysed (depending on subject availability) for example between day 25 and day 29 for whole patient population, you can calculate for all subjects fraction of AUC between time 0 and 25 day as a key parameter for groups comparison etc.

Depending on drug between subject variability of elimination phase its common situation that even all participants (single dose studies) are available in all and the same last sampling times regardless of that in last sampling point concentration of the drug is not always observed. That means in many studies last sampling points are missing (not observed) because between subject variability and its typical feature of PK studies. Im not sure in which PK phase you take last sample in your study but if its simply late elimination phase difference between days 26-29 have no real impact on final findings because so long sampling (4 weeks). especially if you will observe some concentrations in all sampling times. Im not sure what is goal of the study and what kind of administration will be done but I am more interested in the small number of sampling points than differences in timing of Clast in your case.

Best regards

Tomasz

Dear Ibrahim A Komeil,

Important to explain distribution of the drug and metabolites into tissues or organs is knowledge about concentration of the molecule in the analysed organ/tissue. Without metabolites standards you cant check what is concentration of the metabolite in selected organs. So you cant say what percent of the drug is available for the organ in specific time point. Please remember its dynamic process. That means in any time point (for example after single dose) concentration of the drug and metabolites changing not only in blood plasma but changing in all organs and tissues which are available for that drug and metabolites. See example on table 1:

Moreover metabolites distribution not must be correlated by linear way with drug elimination (if I correctly understand your question) especially if there is more than one metabolite and more than 1 organ tissue were distribution ongoing. Many different mechanisms can make difficult to analyse metabolites profiles (enantioselective metabolism, enterohepatic circulation of the metabolites etc.). So best idea to explain metabolites distribution is analysis in organs based on appropriate analytical standards.

Some documents at link below could be useful for you especially related to some aspects of methodology.

https://www.ema.europa.eu/en/vich-gl48-studies-evaluate-metabolism-residue-kinetics-veterinary-drugs-food-producing-animals

Best regards,

Tomasz

Certainly any pharmaceutic laboratory may perform such studies. In any case, you should ask more precise information to Carmelo Scarpignato

Dear Salim Ansari,

I think such paper could be interesting for you before you start your work

https://www.ema.europa.eu/en/documents/presentation/presentation-blood-brain-barrier-maturation-implications-drug-development_en.pdf

Model which you want to optimize could not be easy because different populations of cells and some times cells & phys/chem mechanisms could take part in different phases of PK:

a) absorption

b) distribution

c) reabsorption

d) elimination

e) recirculation (enterohepatic for example or with saliva etc)

Of course its only "general list" of mechanisms, because still many discrete mechanisms related to many drugs are unknown. So in consequence in case of one drug full model which represent all cells which take part in PK mechanisms could be tricky. Some cells, organs take part in PK and biased PK only indirectly (chronopharmacokinetics etc), so definition of the model should be very precise. Maybe easier way to do it for many drugs is take only some cells which take part in one from (a-e) phases or one mechanism. Holistic drug model related to aging/PK, based on different population of cells not exist. Validation of such model could be possible for drugs with simple and predictable PK. For more complicated PK models (all phases are present a-e) validation of the model could be really tricky. Please remember that aging process modify cells and therefore PK related to that cells but aging changing factors which are critical but not directly related to the cells (blood perfusion, blood biochemistry, GIT changes etc etc). They can affect PK directly (phys/chem) but they are not linked with cells .... for example gastric emptying rate. Idea for structuring such model is fingerprinting all critical populations of cells and make some kind of mapping. Hope some my thoughts will be useful ... :)

Best regards

Tomasz

Dear Benedict:

NONMEM and other parametric methods are for the statisticians and the industry, who should care more but don't. If you want to take the best care of individual patients at the bedside, with software that develops dosage regimens to hit a specifically selected target (not a range) you will never be able to do this with the usual parametric PK/PD modeling methods used by the industry. Only nonparametric (NP) modeling approaches can give you the multiple predictions required for multiple model (MM) dosage design in order to find the dose which minimizes the weighted squared error in target achievement. Parametric models have only one version (based on the mean parameter values), so there is nothing to optimize.

Also, NONMEM only calculates the likelihood function in an approximate way, and thus gives wrong results which result in wrong doses, nor how inefficient it is in the information it gets from the raw data. It should take only 4 times as many sublects to redice the standard deviations of model parameter estimates by half. With NONMEM it takes 16 times as many! They never check things like this, so they never see how much in error their results can be. Maximally precise dosage, which the industry never cares about but should, is what they need Their problem is that parametric methods are totally blind to the issue of dosage precision. This may well explain the total lack of any apparent interest in this issue shown by the industry. also. I have asked people whether or not it bothers them that they use methods with only approximate calculations of the likelihood. They say no, probably because they have never checked how much off they can be. Pharmacometrics is a branch of conventional statistics, and statisticians deal in groups, not individual patients. Good adaptive control comes from the military, who care a good deal about hitting targets with maximum precision.

NP models, exact likelihoods, and MM dosage design are what patients need to get maximally precise dosing. NONMEM is for the industry, and is obsolete. The industry also would benefit greatly from using the more capable and general NP approaches. More info from the web site www.lapk.org, and in the book, edited by Dr. Michael Neely and myself, "Individualized Drug Therapy for Patients", at Amazon or Elsevier.

you could try using a cell scrapper too to physically remove the cells from the plate and then use methanol to lyse it further in an eppendorf tube

Dear Sushil,

The 1/Y^2 weighing gives more weight to the data-points with a lower value. This is generally beneficial with concentration data because the error is proportional to the measured value (the absolute error is smaller for lower concentrations) and concentrations drop significantly over time, resulting in a wide range of concentrations in a profile. Without weighing the high concentrations over-influence the model fit.

As already noted the choice of weighing is usually based on AIC or Schwarz criterion.

You can also look at the residual errors (predicted vs observed values) themselves. If the error is not distributed as evenly around lower concentrations (long times) as it is around higher concentrations, you probably need to use weighing. With the right weighing, the error should be more evenly distributed around obseved values at all concentrations.

regarding 2 compartment model there are several volume of distribution (V), but none of them considered the best or actual. in another word it depends on timing or condition " which V is suitable at this time or condition". ex. when the drug is given in multi-dose and reach ss to calculate Cp ss or amount at ss use Vss, however, when the drug is given as bolus and you need to calculate amount at elimination phase use Vz

Thanks a lot Jake !

Dear Akshil Mehta

Its complex question, first its depends on what kind of anticancer class or drug we talking. In case of biological drugs its theoretically possible. Current MOA of CSF cover binding to the G-CSF receptor and stimulation the production of neutrophils in the bone marrow. In case of biological drugs PK interactions could be possible if in case of biological drug (BD) key element of receptor mediated clearance will depends on neutrophils levels. Then PK interaction between CSF and BD is possible and could have impact on clearance.

Common ADR after CSF treatment is leukocytosis. So in case of drugs which target is localized on any class of leucocytes (and strong target mediated drug clearance) CSF may have impact on total clearance of the drug. Please remember that nature of PK interactions with biologics is different that possible interactions with synthetic drugs. In case of interaction with biologics PD is closely linked to PK so very often impact on PD from one drug could results PK changes in other. So depending on drug class both PK and PD effects after CSF treatment could be important to predict or explain possible impact on PK changes in another drug product. Please remember that Granulocyte-colony stimulating factor affects the balance in the production of anti-inflammatory cytokines. It could be important for possible interactions with other drugs especially biologics. Personally I think its peak of the iceberg because our knowledge about possible PD and PK interactions still grooving, see below some helpful links:

Renal interactions: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC90583/pdf/ac001947.pdf

PD interaction: https://hal.archives-ouvertes.fr/hal-01137198/document

Interactions FDA doc. declarations: https://www.accessdata.fda.gov/drugsatfda_docs/label/2012/103353s5147lbl.pdf

non-Hodgkin's lymphoma: https://www.ncbi.nlm.nih.gov/pubmed/16930137

Other interactions see below (source: https://www.drugbank.ca/drugs/DB00099):

1. The risk or severity of pulmonary toxicity can be increased when Filgrastim is combined with Bleomycin.

2. The risk or severity of pulmonary toxicity can be increased when Filgrastim is combined with Cyclophosphamide.

3. The risk or severity of neutropenia can be increased when Filgrastim is combined with Topotecan.

4. The risk or severity of peripheral neuropathy can be increased when Filgrastim is combined with Vinblastine.

5. The risk or severity of peripheral neuropathy can be increased when Filgrastim is combined with Vincamine.

6. The risk or severity of peripheral neuropathy can be increased when Filgrastim is combined with Vincristine.

7. The risk or severity of peripheral neuropathy can be increased when Filgrastim is combined with Vindesine.

8. The risk or severity of peripheral neuropathy can be increased when Filgrastim is combined with Vinflunine.

9. The risk or severity of peripheral neuropathy can be increased when Filgrastim is combined with Vinorelbine.

10. The risk or severity of peripheral neuropathy can be increased when Filgrastim is combined with Vintafolide.

Hope some ideas will be useful in your studies,

Best regards

Tomasz

Dear Salah Adlat,

There is no much information about this compound. Hope some basic information from Toxnet and PubMed Compound could be enough to start your research ....

Best regards

Tomasz

Just have a look at candesartano drug-drug interactions at the link below